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JMLR
2012
13 years 8 months ago
Universal Measurement Bounds for Structured Sparse Signal Recovery
Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
Nikhil S. Rao, Ben Recht, Robert D. Nowak
CORR
2008
Springer
98views Education» more  CORR 2008»
15 years 5 months ago
Sparse Recovery by Non-convex Optimization -- Instance Optimality
In this note, we address the theoretical properties of p, a class of compressed sensing decoders that rely on p minimization with p (0, 1) to recover estimates of sparse and compr...
Rayan Saab, Özgür Yilmaz
SIAMSC
2010
215views more  SIAMSC 2010»
15 years 4 months ago
A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation
We propose a fast algorithm for solving the ℓ1-regularized minimization problem minx∈Rn µ x 1 + Ax − b 2 2 for recovering sparse solutions to an undetermined system of linea...
Zaiwen Wen, Wotao Yin, Donald Goldfarb, Yin Zhang
CORR
2010
Springer
208views Education» more  CORR 2010»
15 years 2 months ago
Real-time Robust Principal Components' Pursuit
In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursui...
Chenlu Qiu, Namrata Vaswani
SIAMIS
2011
15 years 16 days ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch