2.2k citations · 5.4k across the 90 of their papers we have counts for
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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…
Blind Compressed Sensing Over a Structured Union of Subspaces
Jorge Silva, Minhua Chen, Yonina C. Eldar +2
This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sen…
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…
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,…