A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm

Our contributions stand on proposing a novel optimization problem, which takes advantage of the low-rank kernel approximations and on introducing a proximal gradient algo- rithm

This is achieved through a parameterization of similarity functions by convex combinations of sparse rank-one matrices, together with the use of a greedy approximate

In Section 2.4 of [8], the authors propose and analyze a criterion to handle the case of unknown variance in the setting where the rank q of X is strictly smaller than the sample

We use this approach within a filtering framework and prove the- oretical convergence results for the resulting method, denoted as sequential kernel herding (SKH), giving one of

This paper showcases the theoretical and numerical performance of the Sliding Frank-Wolfe, which is a novel optimization algorithm to solve the BLASSO sparse spikes

trace norm, Riemannian optimization, trust region, regularization path, predictor- corrector on quotient manifold, matrix completion, multivariate linear regression.. AMS

Parmi les techniques possibles pour les traitements de l'eau, le procédé d'adsorption par des adsorbants solides présente le potentiel comme l'une des méthodes les plus

La gure 17.6 repr esente en fonction de l' energie du photon ISR, la proportion d' ev enements pour lesquels ces sous-d eclenchements sont allum es parmi les  ev enements