187 citations · 241 across the 8 of their papers we have counts for
8 papers
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…
An MDL framework for sparse coding and dictionary learning
Ignacio Ramírez, Guillermo Sapiro
The power of sparse signal modeling with learned over-complete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inf…
Low-rank data modeling via the Minimum Description Length principle
Ignacio Ramírez, Guillermo Sapiro
Robust low-rank matrix estimation is a topic of increasing interest, with promising applications in a variety of fields, from computer vision to data mining and recommender systems…
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…
Sparse coding and dictionary learning based on the MDL principle
Ignacio Ramírez, Guillermo Sapiro
The power of sparse signal coding with learned dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inference and machi…
C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework
Pablo Sprechmann, Ignacio Ramírez, Guillermo Sapiro +1
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression prob…
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 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,…