Publications (4)
Universal Regularizers For Robust Sparse Coding and Modeling
Ignacio Ramirez, Guillermo Sapiro
Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, an…
Collaborative Sources Identification in Mixed Signals via Hierarchical Sparse Modeling
Pablo Sprechmann, Ignacio Ramirez, Pablo Cancela +1
A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical s…
Binary Matrix Factorization via Dictionary Learning
Ignacio Ramirez
Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a part…
Collaborative Hierarchical Sparse Modeling
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapiro +1
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is done by solving an l_1-regularized linear regression problem,…