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We propose a new method for robust PCA -- the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support.
Matrix Analysis
Rajendra Bhatia · 1997
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Statistical modeling of complex backgrounds for foreground object detection
Liyuan Li, Weimin Huang, IY-H Gu, and Qi Tian · 2004
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The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices
Zhouchen Lin, Minming Chen, and Yi Ma · 2010
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Robust principal component analysis?
Emmanuel J. Candès, Xiaodong Li, Yi Ma, and John Wright · 2011
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Rank-sparsity incoherence for matrix decomposition
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, and Alan S. Willsky · 2011
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Robust matrix decomposition with sparse corruptions
Daniel Hsu, Sham M Kakade, and Tong Zhang · 2011
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Holistic 3d reconstruction of urban structures from low-rank textures
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Tensor Methods for Learning Latent Variable Models
A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky · 2012
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Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
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Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
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László Erdős, Antti Knowles, Horng-Tzer Yau, and Jun Yin · 2013
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On the provable convergence of alternating minimization for matrix completion
Moritz Hardt · 2013
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Low-rank matrix completion using alternating minimization
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi · 2013
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Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi · 2013
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Sparse additive text models with low rank background
Lei Shi · 2013
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