Learning Co-Sparse Analysis Operators with Separable Structures

To summarize, this experi- ment has shown that the sparseFEM algorithms seem to be good compromises be- tween sparse-kmeans and Clustvarsel /Selvarclust in term of variable

Normalized amplitude (top) and phase (bottom) of the mea- sured transfer functions of the VECSEL CEO frequency (thick blue line) and output power (thin red line) obtained for a

L’échographie hépatique montre soit une tumeur hypo-échogène quand elle est de petite taille, soit une masse hétérogène à centre nécrosé dans les grosses tumeurs.Ce sont

Dictionary learning algorithms often fail at that task because of the imper- fection of the sparse representation step: when the dictionary is fixed, sparse approximation

Support Vector Machines (SVMs) are well-established Ma- chine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed

While we have provided some theoretical arguments (asymptotic expansion in the finite-dimensional case, convergence to optimal predictions in the infinite- dimensional case), a

Globally the data clustering slightly deteriorates as p grows. The biggest deterioration is for PS-Lasso procedure. The ARI for each model in PS-Lasso and Lasso-MLE procedures

This quasi- isometry property associated to a given sparse dictionary is quantified in terms of each of the sparsity measures presented in Section III, namely the