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FOCM
2006
43views more  FOCM 2006»
14 years 10 months ago
Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions
Emmanuel J. Candès, Justin K. Romberg
SIAMJO
2011
14 years 1 months ago
Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-ran...
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Pa...
JACM
2011
152views more  JACM 2011»
14 years 1 months ago
Robust principal component analysis?
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component i...
Emmanuel J. Candès, Xiaodong Li, Yi Ma, Joh...
CVPR
2011
IEEE
14 years 5 months ago
Accelerated Low-Rank Visual Recovery by Random Projection
Exact recovery from contaminated visual data plays an important role in various tasks. By assuming the observed data matrix as the addition of a low-rank matrix and a sparse matri...
Yadong Mu, Jian Dong, Xiaotong Yuan, Shuicheng Yan