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TIT
2010
130views Education» more  TIT 2010»
10 years 8 months ago
The power of convex relaxation: near-optimal matrix completion
This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a gr...
Emmanuel J. Candès, Terence Tao
JMLR
2010
147views more  JMLR 2010»
10 years 8 months ago
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
CVPR
2011
IEEE
10 years 8 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
SIAMJO
2010
246views more  SIAMJO 2010»
10 years 11 months ago
A Singular Value Thresholding Algorithm for Matrix Completion
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood a...
Jian-Feng Cai, Emmanuel J. Candès, Zuowei S...
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