G
Gilles Gasso
Researcher at Intelligence and National Security Alliance
Publications - 77
Citations - 1139
Gilles Gasso is an academic researcher from Intelligence and National Security Alliance. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 12, co-authored 69 publications receiving 928 citations. Previous affiliations of Gilles Gasso include Institut national des sciences appliquées de Rouen.
Papers
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Journal ArticleDOI
Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
TL;DR: Experimental results demonstrate the effectiveness of the proposed generic framework compared to existing algorithms, including iterative reweighted least-squares methods, and several algorithms in the literature dealing with nonconvex penalties are particular instances of the algorithm.
Journal ArticleDOI
Histogram of gradients of time-frequency representations for audio scene classification
Alain Rakotomamonjy,Gilles Gasso +1 more
TL;DR: The approach for classifying acoustic scenes is based on transforming the audio signal into a time-frequency representation and then in extracting relevant features about shapes and evolutions of time- frequency structures based on histogram of gradients that are subsequently fed to a multi-class linear support vector machines.
Journal ArticleDOI
A multiple kernel framework for inductive semi-supervised SVM learning
TL;DR: The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Difference of Convex functions) programming.
Journal ArticleDOI
Palmprint recognition with an efficient data driven ensemble classifier
TL;DR: It turns out that for this kind of data, the use of weak classifiers learned over nearly incoherent features is very efficient and an empirical analysis of the parameters involved in the random subspace technique to guide the user in the choice of the appropriate hyper-parameters.
Proceedings Article
Partial Optimal Tranport with applications on Positive-Unlabeled Learning
TL;DR: The first application of optimal transport in this context is presented and it is highlighted that partial Wasserstein-based metrics prove effective in usual PU learning settings and then demonstrated that partial Gromov-Wasserstein metrics is efficient in scenario where point clouds come from different domains or have different features.