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Online Adaptive Statistical Compressed Sensing of Gaussian Mixture Models
Julio Duarte-Carvajalino, Guillermo Sapiro, Guoshen Yu +1
A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses non-adaptive measurements, from which an on…
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…
A convex model for non-negative matrix factorization and dimensionality reduction on physical space
Ernie Esser, Michael Möller, Stanley Osher +2
A collaborative convex framework for factoring a data matrix into a non-negative product , with a sparse coefficient matrix , is proposed. We restrict the columns of the…
Statistical Compressed Sensing of Gaussian Mixture Models
Guoshen Yu, Guillermo Sapiro
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distributi…