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ICASSP
2011
IEEE
12 years 8 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»
12 years 11 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
12 years 11 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
12 years 12 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»
13 years 2 months 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»
13 years 4 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»
14 years 1 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, ...