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» Sparse Recovery Using Sparse Random Matrices
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CORR
2010
Springer
103views Education» more  CORR 2010»
14 years 10 months ago
Robust Matrix Decomposition with Outliers
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from th...
Daniel Hsu, Sham M. Kakade, Tong Zhang
SCALESPACE
2007
Springer
15 years 4 months ago
Non-negative Sparse Modeling of Textures
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...
Gabriel Peyré
ICASSP
2009
IEEE
15 years 4 months ago
A compressive sensing approach to object-based surveillance video coding
This paper studies the feasibility and investigates various choices in the application of compressive sensing (CS) to object-based surveillance video coding. The residual object e...
Divya Venkatraman, Anamitra Makur
TIT
2010
128views Education» more  TIT 2010»
14 years 4 months ago
Shannon-theoretic limits on noisy compressive sampling
In this paper, we study the number of measurements required to recover a sparse signal in M with L nonzero coefficients from compressed samples in the presence of noise. We conside...
Mehmet Akçakaya, Vahid Tarokh
COMPUTING
2007
103views more  COMPUTING 2007»
14 years 10 months ago
An algebraic approach for H-matrix preconditioners
Hierarchical matrices (H-matrices) approximate matrices in a data-sparse way, and the approximate arithmetic for H-matrices is almost optimal. In this paper we present an algebrai...
S. Oliveira, F. Yang