International audienceSparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present contribution lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance maps. These generalize Group LASSO and the previously introduced Elitist LASSO by introducing more flexibility in the coefficient domain modeling. We study experimentally the performances of corresponding shrinkage operators in terms of significance map estimation in the orthogonal basis case. We also study their performance in the overcomplete situation, using iterative thresholding
Numerous fields of applied sciences and industries have been witnessing a process of digitisation ov...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
International audienceThe paper provides a formal description of the sparsity of a representation vi...
International audienceSparse and structured signal expansions on dictionaries can be obtained throug...
nombre de pages : 14International audienceSparse regression often uses $\ell_p$ norm priors (with p<...
Numerous fields of applied sciences and industries have been recently witnessing a process of digiti...
nombre de pages : 27 To appear in: Applied and Computational Harmonic Analysis DOI : 10.1016/j.acha....
AbstractMixed norms are used to exploit in an easy way, both structure and sparsity in the framework...
International audienceStructured sparsity has recently emerged in statistics, machine learning and s...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
International audienceSparse estimation methods are aimed at using or obtaining parsimonious represe...
International audienceWe consider the empirical risk minimization problem for linear supervised lear...
AbstractStructured sparsity approaches have recently received much attention in the statistics, mach...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
Model sparsification in deep learning promotes simpler, more interpretable models with fewer paramet...
Numerous fields of applied sciences and industries have been witnessing a process of digitisation ov...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
International audienceThe paper provides a formal description of the sparsity of a representation vi...
International audienceSparse and structured signal expansions on dictionaries can be obtained throug...
nombre de pages : 14International audienceSparse regression often uses $\ell_p$ norm priors (with p<...
Numerous fields of applied sciences and industries have been recently witnessing a process of digiti...
nombre de pages : 27 To appear in: Applied and Computational Harmonic Analysis DOI : 10.1016/j.acha....
AbstractMixed norms are used to exploit in an easy way, both structure and sparsity in the framework...
International audienceStructured sparsity has recently emerged in statistics, machine learning and s...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
International audienceSparse estimation methods are aimed at using or obtaining parsimonious represe...
International audienceWe consider the empirical risk minimization problem for linear supervised lear...
AbstractStructured sparsity approaches have recently received much attention in the statistics, mach...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
Model sparsification in deep learning promotes simpler, more interpretable models with fewer paramet...
Numerous fields of applied sciences and industries have been witnessing a process of digitisation ov...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
International audienceThe paper provides a formal description of the sparsity of a representation vi...