Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty

no code implementations • 6 Sep 2016 • Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay

However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population.

Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging

no code implementations • 31 May 2016 • Fouad Hadj-Selem, Tommy Lofstedt, Elvis Dohmatob, Vincent Frouin, Mathieu Dubois, Vincent Guillemot, Edouard Duchesnay

Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties but reasonable precision requires a small smoothing parameter, which slows down the convergence speed.

regression

Predictive support recovery with TV-Elastic Net penalty and logistic regression: an application to structural MRI

no code implementations • 21 Jul 2014 • Mathieu Dubois, Fouad Hadj-Selem, Tommy Lofstedt, Matthieu Perrot, Clara Fischer, Vincent Frouin, Edouard Duchesnay

This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm.

Using n-grams models for visual semantic place recognition

no code implementations • 21 Mar 2014 • Mathieu Dubois, Frenoux Emmanuelle, Philippe Tarroux

This paper presents our system and the Bayesian filtering algorithm.

Visual Place Recognition