Rémi Flamary

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I am associate professor at Nice-Sophia Antipolis University in the Departement of Electronics and in the Lagrange Laboratory. This laboratory is part of the Observatoire de la Côte d'Azur. I was previously a PhD student and teaching assistant at the LITIS Laboratory and my PhD advisor was Alain Rakotomamonjy at Rouen University.

On this website, you can find a list of my publications and download the corresponding software/code. Some of my teaching material is also available (in french).

Research Interests

  • Machine Learning
    • Kernel methods, Support Vector Machines
    • Sparsity, variable selection, mixed norm
    • Data representation, kernel learning
  • Statistical signal processing
    • Classification and segmentation of signals and images
    • Filter learning
    • Sparse and non-convex optimization
  • Applications
    • Biomedical engineering, Brain-Computer Interfaces
    • Remote sensing and hyperspectral Imaging
    • Astronomical image processing

Recent work

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, 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 },
 year = { 2014 } 
 } 
Niaf, E., Flamary, R., Rouviere, 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 Rouviere, 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 } 
 } 
Laporte, L., Flamary, R., Canu, S., Déjean, S., Mothe, J., "Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM", IEEE Transactions on Neural Networks and Learning Systems, 2013.
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 = { IEEE Transactions on Neural Networks and Learning Systems },
 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 } 
 } 

News

AMOR project is online

2013-12-04

The AMOR project is a Young researchers project that is financed by GdR ISIS and the GRETSI association.

The wep page of the project is now available here.

Talk about learning with infinitely many features

2013-01-02

I have been invited to present our work about learning with infinitely many features at a GDR ISIS reunion.

The slides of the presentation (in english) are now available here.

New SVM Toolbox

2012-09-10

The code for my linear SVM toolbox is now available in the software section of the website. It can learn linear SVM with a wide class of regularization terms such as the l1 norm or the l1-lp mixed norms.

This toolbox is in Matlab and the solver used is a Forward-Backward Splitting algorithm from the paper FISTA.

Go to the G-SVM page for more information and for downloading the source code.