Rémi Flamary
Professional website
English
Français
ZorglangueContact
Phone: +33 (0)4 92 07 63 80Email:
Publications
Recent Journals Conferences Others All
All publications
| Flamary, R., Aime, C., "Optimization of starshades: focal plane versus pupil plane", Astronomy and Astrophysics, pp 1-10, 2014. |
| Abstract: We search for the best possible transmission for an external occulter coronagraph that is dedicated to the direct observation of terrestrial exoplanets. We show that better observation conditions are obtained when the flux in the focal plane is minimized in the zone in which the exoplanet is observed, instead of the total flux received by the telescope. We describe the transmission of the occulter as a sum of basis functions. For each element of the basis, we numerically computed the Fresnel diffraction at the aperture of the telescope and the complex amplitude at its focus. The basis functions are circular disks that are linearly apodized over a few centimeters (truncated cones). We complemented the numerical calculation of the Fresnel diffraction for these functions by a comparison with pure circular discs (cylinder) for which an analytical expression, based on a decomposition in Lommel series, is available. The technique of deriving the optimal transmission for a given spectral bandwidth is a classical regularized quadratic minimization of intensities, but linear optimizations can be used as well. Minimizing the integrated intensity on the aperture of the telescope or for selected regions of the focal plane leads to slightly different transmissions for the occulter. For the focal plane optimization, the resulting residual intensity is concentrated behind the geometrical image of the occulter, in a blind region for the observation of an exoplanet, and the level of background residual starlight becomes very low outside this image. Finally, we provide a tolerance analysis for the alignment of the occulter to the telescope which also favors the focal plane optimization. This means that telescope offsets of a few decimeters do not strongly reduce the efficiency of the occulter. |
BibTeX:
@article{flamary2014starshade,
author = { Flamary, R. and Aime, C.},
title = {Optimization of starshades: focal plane versus pupil plane},
journal = { Astronomy and Astrophysics},
pages = { 1-10},
year = {2014}
} |
| Boisbunon, A., Flamary, R., Rakotomamonjy, A., Giros, A., Zerubia, J., "Active set strategy for high-dimensional non-convex sparse optimization problems", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2014. |
| Abstract: In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set strategy that is easily scalable and can be computed in parallel. We apply it to a toy image and a satellite image where the aim is to detect all the boats in a harbor. These results show the benefit of using nonconvex penalties, such as the log-sum penalty, over the convex l1 penalty. |
BibTeX:
@inproceedings{boisbunon2014largescale,
author = {Boisbunon, A. and Flamary, R. and Rakotomamonjy, A. and Giros, A. and Zerubia, J.},
title = {Active set strategy for high-dimensional non-convex sparse optimization problems},
booktitle = {IEEE Workshop in Machine Learning for Signal Processing (MLSP)},
year = {2014}
} |
| Niaf, E., Flamary, R., Rakotomamonjy, A., Rouvière, O., Lartizien, C., "SVM with feature selection and smooth prediction in images: application to CAD of prostate cancer", IEEE International Conference on Image Processing (ICIP), 2014. |
Abstract: We propose a new computer-aided detection scheme for prostate cancer screening on multiparametric magnetic resonance (mp-MR) images. Based on an annotated training
database of mp-MR images from thirty patients, we train
a novel support vector machine (SVM)-inspired classifier
which simultaneously learns an optimal linear discriminant
and a subset of predictor variables (or features) that are most
relevant to the classification task, while promoting spatial
smoothness of the malignancy prediction maps. The approach uses a  -norm in the regularization term of the optimization problem that rewards sparsity. Spatial smoothness is
promoted via an additional cost term that encodes the spatial
neighborhood of the voxels, to avoid noisy prediction maps.
Experimental comparisons of the proposed -Smooth SVM
scheme to the regular  -SVM scheme demonstrate a clear
visual and numerical gain on our clinical dataset. |
BibTeX:
@inproceedings{niaf2014svmsmooth,
author = {Niaf, E. and Flamary, R. and Rakotomamonjy, A. and Rouvière, O. and Lartizien, C.},
title = {SVM with feature selection and smooth prediction in images: application to CAD of prostate cancer},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = {2014}
} |
| Lehaire, J., Flamary, R., Rouvière, O., Lartizien, C., "Computer-aided diagnostic for prostate cancer detection and characterization combining learned dictionaries and supervised classification", IEEE International Conference on Image Processing (ICIP), 2014. |
| Abstract: This paper aims at presenting results of a computer-aided diagnostic (CAD) system for voxel based detection and characterization of prostate cancer in the peripheral zone based on multiparametric magnetic resonance (mp-MR) imaging. We propose an original scheme with the combination of a feature extraction step based on a sparse dictionary learning (DL) method and a supervised classification in order to discriminate normal (N), normal but suspect (NS) tissues as well as different classes of cancer tissue whose aggressiveness is characterized by the Gleason score ranging from 6 (GL6) to 9 (GL9). We compare the classification performance of two supervised methods, the linear support vector machine (SVM) and the multinomial logistic regression (MLR) classifiers in a binary classification task. Classification performances were evaluated over an mp-MR image database of 35 patients where each voxel was labeled, based on a ground truth, by an expert radiologist. Results show that the proposed method in addition to being interpretable thanks to the sparse representation of the voxels compares favorably (AUC>0.8) with recent state of the art performances. Preliminary results on example patients data also indicate that the outputs cancer probability maps are correlated to the Gleason score. |
BibTeX:
@inproceedings{lehaire2014dicolearn,
author = {Lehaire, J. and Flamary, R. and Rouvière, O. and Lartizien, C.},
title = {Computer-aided diagnostic for prostate cancer detection and characterization combining learned dictionaries and supervised
classification},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = {2014}
} |
| Ferrari, A., Mary, D., Flamary, R., Richard, C., "Distributed image reconstruction for very large arrays in radio astronomy", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014. |
| Abstract: Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox. Their large number of receptors (up to millions) allow theoretically unprecedented high imaging resolution. In the same time, the ultra massive amounts of samples makes the data transfer and computational loads (correlation and calibration) order of magnitudes too high to allow any currently existing image reconstruction algorithm to achieve, or even approach, the theoretical resolution. We investigate here decentralized and distributed image reconstruction strategies which select, transfer and process only a fraction of the total data. The loss in MSE incurred by the proposed approach is evaluated theoretically and numerically on simple test cases. |
BibTeX:
@inproceedings{ferrari2014distributed,
author = {Ferrari, A. and Mary, D. and Flamary, R. and Richard, C.},
title = {Distributed image reconstruction for very large arrays in radio astronomy},
booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
year = {2014}
} |
| Courty, N., Flamary, R., Tuia, D., "Domain adaptation with regularized optimal transport", European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2014. |
| Abstract: We present a new and original method to solve the domain adaptation problem using optimal transport. By searching for the best transportation plan between the probability distribution functions of a source and a target domain, a non-linear and invertible transformation of the learning samples can be estimated. Any standard machine learning method can then be applied on the transformed set, which makes our method very generic. We propose a new optimal transport algorithm that incorporates label information in the optimization: this is achieved by combining an efficient matrix scaling technique together with a majoration of a non-convex regularization term. By using the proposed optimal transport with label regularization, we obtain significant increase in performance compared to the original transport solution. The proposed algorithm is computationally efficient and effective, as illustrated by its evaluation on a toy example and a challenging real life vision dataset, against which it achieves competitive results with respect to state-of-the-art methods. |
BibTeX:
@inproceedings{courty2014domain,
author = {Courty, N. and Flamary, R. and Tuia, D.},
title = {Domain adaptation with regularized optimal transport},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
year = {2014}
} |
| Boisbunon, A., Flamary, R., Rakotomamonjy, A., "Active set strategy for high-dimensional non-convex sparse optimization problems", International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014. |
| Abstract: The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions. |
BibTeX:
@inproceedings{boisbunon2014active,
author = {Boisbunon, A. and Flamary, R. and Rakotomamonjy, A.},
title = {Active set strategy for high-dimensional non-convex sparse optimization problems},
booktitle = {International Conference on Acoustic, Speech and Signal Processing (ICASSP)},
year = {2014}
} |
| Flamary, R., Jrad, N., Phlypo, R., Congedo, M., Rakotomamonjy, A., "Mixed-Norm Regularization for Brain Decoding", Computational and Mathematical Methods in Medicine, Vol. 2014, N. 1, pp 1-13, 2014. |
| Abstract: This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly. |
BibTeX:
@article{flamary2014mixed,
author = {Flamary, R. and Jrad, N. and Phlypo, R. and Congedo, M. and Rakotomamonjy, A.},
title = {Mixed-Norm Regularization for Brain Decoding},
journal = {Computational and Mathematical Methods in Medicine},
volume = {2014},
number = {1},
pages = {1-13},
year = {2014}
} |
| Niaf, E., Flamary, R., Rouvière, O., Lartizien, C., Canu, S., "Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging", Image Processing, IEEE Transactions on, Vol. 23, N. 3, pp 979-991, 2014. |
| Abstract: Building an accurate training database is challenging in supervised classification. For instance, in medical imaging, radiologists often delineate malignant and benign tissues without access to the histological ground truth, leading to uncertain data sets. This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here are both qualitative (a class label) or quantitative (an estimation of the posterior probability). In this context, usual discriminative methods, such as the support vector machine (SVM), fail either to learn a robust classifier or to predict accurate probability estimates. We generalize the regular SVM by introducing a new formulation of the learning problem to take into account class labels as well as class probability estimates. This original reformulation into a probabilistic SVM (P-SVM) can be efficiently solved by adapting existing flexible SVM solvers. Furthermore, this framework allows deriving a unique learned prediction function for both decision and posterior probability estimation providing qualitative and quantitative predictions. The method is first tested on synthetic data sets to evaluate its properties as compared with the classical SVM and fuzzy-SVM. It is then evaluated on a clinical data set of multiparametric prostate magnetic resonance images to assess its performances in discriminating benign from malignant tissues. P-SVM is shown to outperform classical SVM as well as the fuzzy-SVM in terms of probability predictions and classification performances, and demonstrates its potential for the design of an efficient computer-aided decision system for prostate cancer diagnosis based on multiparametric magnetic resonance (MR) imaging. |
BibTeX:
@article{niaf2014kernel,
author = {Niaf, E. and Flamary, R. and Rouvière, O. and Lartizien, C. and Canu, S.},
title = {Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging},
journal = {Image Processing, IEEE Transactions on},
volume = {23},
number = {3},
pages = {979-991},
year = {2014}
} |
| Tuia, D., Volpi, M., Dalla Mura, M., Rakotomamonjy, A., Flamary, R., "Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM", Geoscience and Remote Sensing, IEEE Transactions on, Vol. 52, N. 10, pp 6062-6074, 2014. |
| Abstract: Including spatial information is a key step for successful remote sensing image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results are boosted. In this paper, we consider the triple objective of designing a spatial/spectral classifier, which is compact (uses as few features as possible), discriminative (enhances class separation), and robust (works well in small sample situations). We achieve this triple objective by discovering the relevant features in the (possibly infinite) space of spatial filters by optimizing a margin-maximization criterion. Instead of imposing a filter bank with predefined filter types and parameters, we let the model figure out which set of filters is optimal for class separation. To do so, we randomly generate spatial filter banks and use an active-set criterion to rank the candidate features according to their benefits to margin maximization (and, thus, to generalization) if added to the model. Experiments on multispectral very high spatial resolution (VHR) and hyperspectral VHR data show that the proposed algorithm, which is sparse and linear, finds discriminative features and achieves at least the same performances as models using a large filter bank defined in advance by prior knowledge. |
BibTeX:
@article{tuia2014automatic,
author = {Tuia, D. and Volpi, M. and Dalla Mura, M. and Rakotomamonjy, A. and Flamary, R.},
title = {Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM},
journal = {Geoscience and Remote Sensing, IEEE Transactions on},
volume = {52},
number = {10},
pages = {6062-6074},
year = {2014}
} |
| Laporte, L., Flamary, R., Canu, S., Déjean, S., Mothe, J., "Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM", Neural Networks and Learning Systems, IEEE Transactions on, Vol. 25, N. 6, pp 1118-1130, 2014. |
| Abstract: Feature selection in learning to rank has recently emerged as a crucial issue. Whereas several preprocessing approaches have been proposed, only a few works have been focused on integrating the feature selection into the learning process. In this work, we propose a general framework for feature selection in learning to rank using SVM with a sparse regularization term. We investigate both classical convex regularizations such as l1 or weighted l1 and non-convex regularization terms such as log penalty, Minimax Concave Penalty (MCP) or lp pseudo norm with p lower than 1. Two algorithms are proposed, first an accelerated proximal approach for solving the convex problems, second a reweighted l1 scheme to address the non-convex regularizations. We conduct intensive experiments on nine datasets from Letor 3.0 and Letor 4.0 corpora. Numerical results show that the use of non-convex regularizations we propose leads to more sparsity in the resulting models while prediction performance is preserved. The number of features is decreased by up to a factor of six compared to the l1 regularization. In addition, the software is publicly available on the web. |
BibTeX:
@article{tnnls2014,
author = { Laporte, L. and Flamary, R. and Canu, S. and Déjean, S. and Mothe, J.},
title = {Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM},
journal = { Neural Networks and Learning Systems, IEEE Transactions on},
volume = {25},
number = {6},
pages = {1118-1130},
year = {2014}
} |
| Gao, W., Chen, J., Richard, C., Huang, J., Flamary, R., "Kernel LMS algorithm with Forward-Backward splitting for dictionnary learning", International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2013. |
| Abstract: Nonlinear adaptive filtering with kernels has become a topic of high interest over the last decade. A characteristics of kernel-based techniques is that they deal with kernel expansions whose number of terms is equal to the number of input data, making them unsuitable for online applications. Kernel-based adaptive filtering algorithms generally rely on a two-stage process at each iteration: a model order control stage that limits the increase in the number of terms by including only valuable kernels into the so-called dictionary, and a fil- ter parameter update stage. It is surprising to note that most existing strategies for dictionary update can only incorporate new elements into the dictionary. This unfortunately means that they cannot discard obsolete kernel functions, within the context of a time-varying environment in particular. Recently, to remedy this drawback, it has been proposed to associate an l1-norm regularization criterion with the mean-square error criterion. The aim of this paper is to provide theoretical results on the convergence of this approach. |
BibTeX:
@inproceedings{gao2013kernel,
author = {Gao, W. and Chen, J. and Richard, C. and Huang, J. and Flamary, R.},
title = {Kernel LMS algorithm with Forward-Backward splitting for dictionnary learning},
booktitle = {International Conference on Acoustic, Speech and Signal Processing (ICASSP)},
year = {2013}
} |
| Flamary, R., Rakotomamonjy, A., "Support Vector Machine with spatial regularization for pixel classification", International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines : theory and applications (ROKS), 2013. |
| Abstract: We propose in this work to regularize the output of a svm classifier on pixels in order to promote smoothness in the predicted image. The learning problem can be cast as a semi-supervised SVM with a particular structure encoding pixel neighborhood in the regularization graph. We provide several optimization schemes in order to solve the problem for linear SVM with l2 or l1 regularization and show the interest of the approach on an image classification example with very few labeled pixels. |
BibTeX:
@inproceedings{ROKS2013,
author = { Flamary, R. and Rakotomamonjy, A.},
title = {Support Vector Machine with spatial regularization for pixel classification},
booktitle = { International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines : theory and applications (ROKS)},
year = {2013}
} |
| Tuia, D., Volpi, M., Dalla Mura, M., Rakotomamonjy, A., Flamary, R., "Create the relevant spatial filterbank in the hyperspectral jungle", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2013. |
Abstract: Inclusion of spatial information is known to be beneficial to the classification of hyperspectral images. However, given the high dimensionality of the data, it is difficult to know before hand which are the bands to filter or what are the filters to be applied. In this paper, we propose an active set algorithm based on a  support vector machine that explores the (possibily infinite) space of spatial filters and retrieve automatically the filters that maximize class separation. Experiments on hyperspectral imagery confirms the power of the method, that reaches state of the art performance with small feature sets generated automatically and without prior knowledge. |
BibTeX:
@inproceedings{IGARSS2013,
author = { Tuia, D. and Volpi, M. and Dalla Mura, M. and Rakotomamonjy, A. and Flamary, R.},
title = {Create the relevant spatial filterbank in the hyperspectral jungle},
booktitle = { IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
year = {2013}
} |
| Tuia, D., Flamary, R., Volpi, M., Dalla Mura, M., Rakotomamonjy, A., " Discovering relevant spatial filterbanks for VHR image classification", International Conference on Pattern Recognition (ICPR), 2012. |
| Abstract: In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used are numerous (e.g. textural, morphological, Gabor, wavelets, etc.) and the user must pre-select a family of features, as well as their specific parameters. This results in features spaces that are high dimensional and redundant, thus requiring long and suboptimal feature selection phases. In this paper, we propose to discover the relevant filters as well as their parameters with a sparsity promoting regularization and an active set algorithm that iteratively adds to the model the most promising features. This way, we explore the filters/parameters input space efficiently (which is infinitely large for continuous parameters) and construct the optimal filterbank for classification without any other information than the types of filters to be used. |
BibTeX:
@inproceedings{ICPR2012,
author = { Tuia, D. and Flamary, R. and Volpi, M. and Dalla Mura, M. and Rakotomamonjy, A.},
title = { Discovering relevant spatial filterbanks for VHR image classification},
booktitle = { International Conference on Pattern Recognition (ICPR)},
year = {2012}
} |
| Flamary, R., Rakotomamonjy, A., "Decoding finger movements from ECoG signals using switching linear models", Frontiers in Neuroscience, Vol. 6, N. 29, 2012. |
| Abstract: One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest towards such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECog signals. The difficulty of the problem relies on the fact that there is no simple relation between ECoG signals and finger movements. We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state. Switching models can integrate prior knowledge about the decoding problem and helps in predicting fine and precise movements. Our model is thus based on a first block which estimates which finger is moving and another block which, knowing which finger is moving, predicts the movements of all other fingers. Numerical results that have been submitted to the Competition show that the model yields high decoding performances when the hidden state is well estimated. This approach achieved the second place in the BCI competition with a correlation measure between real and predicted movements of 0.42. |
BibTeX:
@article{frontiers2012,
author = { Flamary, R. and Rakotomamonjy, A.},
title = {Decoding finger movements from ECoG signals using switching linear models},
journal = { Frontiers in Neuroscience},
volume = { 6},
number = { 29},
year = {2012}
} |
| Rakotomamonjy, A., Flamary, R., Yger, F., "Learning with infinitely many features", Machine Learning, Vol. 91, N. 1, pp 43-66, 2012. |
| Abstract: We propose a principled framework for learning with infinitely many
features, situations that are usually induced by continuously parametrized feature
extraction methods. Such cases occur for instance when considering Gabor-based
features in computer vision problems or when dealing with Fourier features for
kernel approximations. We cast the problem as the one of finding a finite subset of
features that minimizes a regularized empirical risk. After having analyzed the optimality conditions of such a problem, we propose a simple algorithm which has the
avour of a column-generation technique. We also show that using Fourier-based features, it is possible to perform approximate infinite kernel learning. Our experimental results on several datasets show the benefits of the proposed approach in several situations including texture classification and large-scale kernelized problems (involving about 100 thousand examples). |
BibTeX:
@article{ml2012,
author = { Rakotomamonjy, A. and Flamary, R. and Yger, F.},
title = {Learning with infinitely many features},
journal = { Machine Learning},
volume = {91},
number = {1},
pages = {43-66},
year = {2012}
} |
| Flamary, R., Tuia, D., Labbé, B., Camps-Valls, G., Rakotomamonjy, A., "Large Margin Filtering", IEEE Transactions Signal Processing, Vol. 60, N. 2, pp 648-659, 2012. |
| Abstract: Many signal processing problems are tackled by filtering the signal for subsequent feature classification or regression. Both steps are critical and need to be designed carefully to deal with the particular statistical characteristics of both signal and noise. Optimal design of the filter and the classifier are typically aborded in a separated way, thus leading to suboptimal classification schemes. This paper proposes an efficient methodology to learn an optimal signal filter and a support vector machine (SVM) classifier jointly. In particular, we derive algorithms to solve the optimization problem, prove its theoretical convergence, and discuss different filter regularizers for automated scaling and selection of the feature channels. The latter gives rise to different formulations with the appealing properties of sparseness and noise-robustness. We illustrate the performance of the method in several problems. First, linear and nonlinear toy classification examples, under the presence of both Gaussian and convolutional noise, show the robustness of the proposed methods. The approach is then evaluated on two challenging real life datasets: BCI time series classification and multispectral image segmentation. In all the examples, large margin filtering shows competitive classification performances while offering the advantage of interpretability of the filtered channels retrieved. |
BibTeX:
@article{ieeesp2012,
author = { Flamary, R. and Tuia, D. and Labbé, B. and Camps-Valls, G. and Rakotomamonjy, A.},
title = {Large Margin Filtering},
journal = { IEEE Transactions Signal Processing},
volume = {60},
number = {2},
pages = {648-659},
year = {2012}
} |
| Niaf, E., Flamary, R., Canu, S., Rouvière, O., Lartizien, C., "Handling learning samples uncertainties in SVM : application to MRI-based prostate cancer Computer-Aided Diagnosis", IEEE International Symposium on Biomedical Imaging , 2012. |
| Abstract: Building an accurate training database is challenging in supervised classification. Radiologists often delineate malignant and benign tissues without access to the ground truth thus leading to uncertain datasets. We propose to deal with this uncertainty by introducing probabilistic labels in the learning stage. We introduce a probabilistic support vector machine (P-SVM) inspired from the regular C-SVM formulation allowing to consider class labels through a hinge loss and probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. Solution is used for both decision and posterior probability estimation. |
BibTeX:
@inproceedings{isbi2012,
author = { Niaf, E. and Flamary, R. and Canu, S. and Rouvière, O. and Lartizien, C.},
title = {Handling learning samples uncertainties in SVM : application to MRI-based prostate cancer Computer-Aided Diagnosis},
booktitle = { IEEE International Symposium on Biomedical Imaging },
year = {2012}
} |
| Rakotomamonjy, A., Flamary, R., Gasso, G. and Canu, S., "lp-lq penalty for sparse linear and sparse multiple kernel multi-task learning", IEEE Transactions on Neural Networks, Vol. 22, N. 8, pp 1307-1320, 2011. |
Abstract: Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share a common
sparsity profile. Such a problem can be addressed through a regularization
framework where the regularizer induces a joint-sparsity pattern
between task decision functions. We follow this principled framework
and focus on  (with  and  ) mixed-norms as sparsity- inducing penalties. Our motivation
for addressing such a larger class of penalty is to adapt the penalty
to a problem at hand leading thus to better performances and better
sparsity pattern. For solving the problem in the general multiple
kernel case, we first derive a variational formulation of the 
penalty which helps up in proposing an alternate optimization algorithm.
Although very simple, the latter algorithm provably converges to
the global minimum of the penalized problem. For
the linear case, we extend existing works considering accelerated
proximal gradient to this penalty. Our contribution in this context
is to provide an efficient scheme for computing the
proximal operator. Then, for the more general case when  , we solve the resulting non-convex problem through a majorization-minimization
approach. The resulting algorithm is an iterative scheme which,
at each iteration, solves a weighted sparse MTL
problem. Empirical evidences from toy dataset and real-word datasets
dealing with BCI single trial EEG classification and protein
subcellular localization show the benefit of the proposed approaches
and algorithms. |
BibTeX:
@article{tnn2011,
author = { Rakotomamonjy, A. and Flamary, R. and Gasso, G. and Canu, S.},
title = {lp-lq penalty for sparse linear and sparse multiple kernel multi-task learning},
journal = { IEEE Transactions on Neural Networks},
volume = {22},
number = {8},
pages = {1307-1320},
year = {2011}
} |
| N. Jrad, M. Congedo, R. Phlypo, S. Rousseau, R. Flamary, F. Yger, A. Rakotomamonjy, "sw-SVM: sensor weighting support vector machines for EEG-based brain–computer interfaces", Journal of Neural Engineering, Vol. 8, N. 5, pp 056004, 2011. |
| Abstract: In many machine learning applications, like brain–computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials. |
BibTeX:
@article{jrad2011swsvm,
author = {N. Jrad and M. Congedo and R. Phlypo and S. Rousseau and R. Flamary and F. Yger and A. Rakotomamonjy},
title = {sw-SVM: sensor weighting support vector machines for EEG-based brain–computer interfaces},
journal = {Journal of Neural Engineering},
volume = {8},
number = {5},
pages = {056004},
year = {2011}
} |
| Flamary, R., Yger, F., Rakotomamonjy, A., " Selecting from an infinite set of features in SVM", European Symposium on Artificial Neural Networks, 2011. |
| Abstract: Dealing with the continuous parameters of a feature extraction method has always been a difficult task that is usually solved by cross-validation. In this paper, we propose an active set algorithm for selecting automatically these parameters in a SVM classification context. Our experiments on texture recognition and BCI signal classification show that optimizing the feature parameters in a continuous space while learning the decision function yields to better performances than using fixed parameters obtained from a grid sampling |
BibTeX:
@inproceedings{ESANN2011,
author = { Flamary, R. and Yger, F. and Rakotomamonjy, A.},
title = { Selecting from an infinite set of features in SVM},
booktitle = { European Symposium on Artificial Neural Networks},
year = {2011}
} |
| Flamary, R., Anguera, X., Oliver, N., " Spoken WordCloud: Clustering Recurrent Patterns in Speech", International Workshop on Content-Based Multimedia Indexing, 2011. |
| Abstract: The automatic summarization of speech recordings is typically carried out as a two step process: the speech is first decoded using an automatic speech recognition system and the resulting text transcripts are processed to create the summary. However, this approach might not be suitable with adverse acoustic conditions or languages with limited training resources. In order to address these limitations, we propose in this paper an automatic speech summarization method that is based on the automatic discovery of patterns in the speech: recurrent acoustic patterns are first extracted from the audio and then are clustered and ranked according to the number of repetitions in the recording. This approach allows us to build what we call a Spoken WordCloud because of its similarity with text-based word-clouds. We present an algorithm that achieves a cluster purity of up to 90% and an inverse purity of 71% in preliminary experiments using a small dataset of connected spoken words. |
BibTeX:
@inproceedings{CBMI2011,
author = { Flamary, R. and Anguera, X. and Oliver, N.},
title = { Spoken WordCloud: Clustering Recurrent Patterns in Speech},
booktitle = { International Workshop on Content-Based Multimedia Indexing},
year = {2011}
} |
| Niaf, E., Flamary, R., Lartizien, C. and Canu, S., "Handling uncertainties in SVM classification", IEEE Workshop on Statistical Signal Processing , 2011. |
| Abstract: This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. The solution provided can be used for both decision and posterior probability estimation. Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances. |
BibTeX:
@inproceedings{ssp2011,
author = { Niaf, E. and Flamary, R. and Lartizien, C. and Canu, S.},
title = {Handling uncertainties in SVM classification},
booktitle = { IEEE Workshop on Statistical Signal Processing },
year = {2011}
} |
| Flamary, R., "Apprentissage statistique pour le signal: applications aux interfaces cerveau-machine", Laboratoire LITIS, Université de Rouen, 2011. |
| Abstract: Brain Computer Interfaces (BCI) require the use of statistical learning methods for signal recognition. In this thesis we propose a
general approach using prior knowledge on the problem at hand
through regularization. To this end, we learn jointly the classifier
and the feature extraction step in a unique optimization problem. We
focus on the problem of sensor selection, and propose several
regularization terms adapted to the problem.
Our first contribution is a filter learning method called large margin filtering. It consists in learning a filtering maximizing the margin between samples of each classe so as to adapt to the properties of the features. In addition, this approach is easy to interpret and can lead to the selection of the most relevant sensors. Numerical experiments on a real life BCI problem and a 2D image classification show the good behaviour of our method both in terms of performance and interpretability. The second contribution is a general sparse multitask learning approach. Several classifiers are learned jointly and discriminant kernels for all the tasks are automatically selected. We propose some efficient algorithms and numerical experiments have shown the interest of our approach. Finally, the third contribution is a direct application of the sparse multitask learning to a BCI event-related potential classification problem. We propose an adapted regularization term that promotes both sensor selection and similarity between the classifiers. Numerical experiments show that the calibration time of a BCI can be drastically reduced thanks to the proposed multitask approach. |
BibTeX:
@phdthesis{thesis2011,
author = { Flamary, R.},
title = {Apprentissage statistique pour le signal: applications aux interfaces cerveau-machine},
school = { Laboratoire LITIS, Université de Rouen},
year = {2011}
} |
| Flamary, R., Labbé, B., Rakotomamonjy, A., "Large margin filtering for signal sequence labeling", International Conference on Acoustic, Speech and Signal Processing 2010, 2010. |
| Abstract: Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal cutoff frequency and phase of the filter that may be different from zero. Two methods are proposed and tested on a toy dataset and on a real life BCI dataset from BCI Competition III. |
BibTeX:
@inproceedings{flamaryicassp210,
author = { Flamary, R. and Labbé, B. and Rakotomamonjy, A.},
title = {Large margin filtering for signal sequence labeling},
booktitle = { International Conference on Acoustic, Speech and Signal Processing 2010},
year = {2010}
} |
| Flamary, R., Labbé, B., Rakotomamonjy, A., "Filtrage vaste marge pour l'étiquetage séquentiel de signaux", Conference en Apprentissage CAp, 2010. |
| Abstract: Ce papier traite de l’étiquetage séquentiel de signaux, c’est-à-dire de discrimination pour des échantillons temporels. Dans ce contexte, nous proposons une méthode d’apprentissage pour un filtrage vaste-marge séparant au mieux les classes. Nous apprenons ainsi de manière jointe un SVM sur des échantillons et un filtrage temporel de ces échantillons. Cette méthode permet l’étiquetage en ligne d’échantillons temporels. Un décodage de séquence hors ligne optimal utilisant l’algorithme de Viterbi est également proposé. Nous introduisons différents termes de régularisation, permettant de pondérer ou de sélectionner les canaux automatiquement au sens du critère vaste-marge. Finalement, notre approche est testée sur un exemple jouet de signaux non-linéaires ainsi que sur des données réelles d’Interface Cerveau-Machine. Ces expériences montrent l’intérêt de l’apprentissage supervisé d’un filtrage temporel pour l’étiquetage de séquence. |
BibTeX:
@inproceedings{flamcap2010,
author = { Flamary, R. and Labbé, B. and Rakotomamonjy, A.},
title = {Filtrage vaste marge pour l'étiquetage séquentiel de signaux},
booktitle = { Conference en Apprentissage CAp},
year = {2010}
} |
| Tuia, D., Camps-Valls, G., Flamary, R., Rakotomamonjy, A., "Learning spatial filters for multispectral image segmentation", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2010. |
| Abstract: We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters |
BibTeX:
@inproceedings{mlsp10,
author = { Tuia, D. and Camps-Valls, G. and Flamary, R. and Rakotomamonjy, A.},
title = {Learning spatial filters for multispectral image segmentation},
booktitle = { IEEE Workshop in Machine Learning for Signal Processing (MLSP)},
year = {2010}
} |
| Flamary, R., Rakotomamonjy, A., Gasso, G., Canu, S., "Selection de variables pour l'apprentissage simultanée de tâches", Conférence en Apprentissage (CAp'09), 2009. |
Abstract: Cet article traite de la sélection de variables pour l’apprentissage simultanée de taches de discrimination SVM . Nous formulons ce problème
comme étant un apprentissage multi-taches avec pour terme de régularisation
une norme mixte de type p2 avec p <1 . Cette dernière permet d’obtenir
des modèles de discrimination pour chaque tâche, utilisant un même
sous-ensemble des variables. Nous proposons tout d’abord un algorithme
permettant de résoudre le problème d’apprentissage lorsque la norme
mixte est convexe (p = 1). Ensuite, à l’aide de la programmation
DC, nous traitons le cas non-convexe (p < 1) . Nous montrons que
ce dernier cas peut être résolu par un algorithme itératif où, à
chaque itération, un problème basé sur la norme mixte 12 est résolu.
Nos expériences montrent l’interêt de la méthode sur quelques problèmes
de discriminations simultanées. |
BibTeX:
@inproceedings{cap09,
author = { Flamary, R. and Rakotomamonjy, A. and Gasso, G. and Canu, S.},
title = {Selection de variables pour l'apprentissage simultanée de tâches},
booktitle = { Conférence en Apprentissage (CAp'09)},
year = {2009}
} |
| R. Flamary, J.L. Rose, A. Rakotomamonjy, S. Canu, "Variational Sequence Labeling", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2009. |
| Abstract: Sequence labeling is concerned with processing an input data sequence and producing an output sequence of discrete labels which characterize it. Common applications includes speech recognition, language processing (tagging, chunking) and bioinformatics. Many solutions have been proposed to partially cope with this problem. These include probabilistic models (HMMs, CRFs) and machine learning algorithm (SVM, Neural nets). In practice, the best results have been obtained by combining several of these methods. However, fusing different signal segmentation methods is not straightforward, particularly when integrating prior information. In this paper the sequence labeling problem is viewed as a multi objective optimization task. Each objective targets a different aspect of sequence labelling such as good classification, temporal stability and change detection. The resulting optimization problem turns out to be non convex and plagued with numerous local minima. A region growing algorithm is proposed as a method for finding a solution to this multi functional optimization task. The proposed algorithm is evaluated on both synthetic and real data (BCI dataset). Results are encouraging and better than those previously reported on these datasets. |
BibTeX:
@inproceedings{mlsp09,
author = { R. Flamary and J.L. Rose and A. Rakotomamonjy and S. Canu},
title = {Variational Sequence Labeling},
booktitle = { IEEE Workshop in Machine Learning for Signal Processing (MLSP)},
year = {2009}
} |
| Flamary, R., Labbé, B., Rakotomamonjy, A., "Large margin filtering for signal segmentation", NIPS Workshop on Temporal Segmentation NIPS Workshop in Temporal Segmentation, 2009. |
| Abstract: |
BibTeX:
@conference{nipsworkshop2009,
author = { Flamary, R. and Labbé, B. and Rakotomamonjy, A.},
title = {Large margin filtering for signal segmentation},
booktitle = { NIPS Workshop on Temporal Segmentation},
howpublished = { NIPS Workshop in Temporal Segmentation},
year = {2009}
} |
| R. Flamary, A. Rakotomamonjy, G. Gasso, S. Canu, "SVM Multi-Task Learning and Non convex Sparsity Measure", The Learning Workshop The Learning Workshop (Snowbird), 2009. |
| Abstract: |
BibTeX:
@conference{snowbird09,
author = { R. Flamary and A. Rakotomamonjy and G. Gasso and S. Canu},
title = {SVM Multi-Task Learning and Non convex Sparsity Measure},
booktitle = { The Learning Workshop},
howpublished = { The Learning Workshop (Snowbird)},
year = {2009}
} |
| Flamary, R., "Filtrage de surfaces obtenues à partir de structures M-Rep (M-Rep obtained surface filtering)", Laboratoire CREATIS-LRMN, INSA de Lyon, 2008. |
| Abstract: |
BibTeX:
@mastersthesis{mrep08,
author = { Flamary, R.},
title = {Filtrage de surfaces obtenues à partir de structures M-Rep (M-Rep obtained surface filtering)},
school = { Laboratoire CREATIS-LRMN, INSA de Lyon},
year = {2008}
} |