735 citations · 778 across the 10 of their papers we have counts for
3 papers · 1 filter
Learning efficient sparse and low rank models
Pablo Sprechmann, Alex M. Bronstein, Guillermo Sapiro
Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approac…
Learning Robust Low-Rank Representations
Pablo Sprechmann, Alex M. Bronstein, Guillermo Sapiro
In this paper we present a comprehensive framework for learning robust low-rank representations by combining and extending recent ideas for learning fast sparse coding regressors w…
Efficient Matrix Completion with Gaussian Models
Flavien Léger, Guoshen Yu, Guillermo Sapiro
A general framework based on Gaussian models and a MAP-EM algorithm is introduced in this paper for solving matrix/table completion problems. The numerical experiments with the sta…