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ICASSP
2011
IEEE
8 years 6 months ago
Low-rank matrix completion with geometric performance guarantees
—The low-rank matrix completion problem can be stated as follows: given a subset of the entries of a matrix, find a low-rank matrix consistent with the observations. There exist...
Wei Dai, Ely Kerman, Olgica Milenkovic
SIAMMAX
2010
164views more  SIAMMAX 2010»
8 years 9 months ago
Uniqueness of Low-Rank Matrix Completion by Rigidity Theory
The problem of completing a low-rank matrix from a subset of its entries is often encountered in the analysis of incomplete data sets exhibiting an underlying factor model with app...
Amit Singer, Mihai Cucuringu
ACCV
2010
Springer
8 years 9 months ago
Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery
We present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recover...
Lun Wu, Arvind Ganesh, Boxin Shi, Yasuyuki Matsush...
CVPR
2011
IEEE
8 years 10 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
CORR
2010
Springer
115views Education» more  CORR 2010»
9 years 27 days ago
Tight oracle bounds for low-rank matrix recovery from a minimal number of random measurements
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix from just a few measurements consisting of linear combinations of the matrix entr...
Emmanuel J. Candès, Yaniv Plan
CORR
2010
Springer
130views Education» more  CORR 2010»
9 years 2 months ago
Stable Principal Component Pursuit
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross spar...
Zihan Zhou, Xiaodong Li, John Wright, Emmanuel J. ...
CVPR
2010
IEEE
1192views Computer Vision» more  CVPR 2010»
9 years 11 months ago
RASL: Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images
This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of im...
Yigang Peng, Arvind Balasubramanian, John Wright, ...
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