Matrix Completion

http://perception.csl.illinois.edu/matrix-rank/references.html

2010年的CVPR最佳paper：Efficient computation of robust low-rank matrix approximations in the presence of missing data using l1 norm.

Tutorials

1. Low-Rank Matrix Recovery: From Theory to Imaging Applications,
   John Wright, Zhouchen Lin, and Yi Ma. Presented at International Conference on Image and Graphics (ICIG), August 2011.

2. Low-Rank Matrix Recovery,
   John Wright, Zhouchen Lin, and Yi Ma. Presented at IEEE International Conference on Image Processing (ICIP), September 2010.

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Theory

1. Robust Principal Component Analysis?,
   Emmanuel Candès, Xiaodong Li, Yi Ma, and John Wright. Journal of the ACM, volume 58, no. 3, May 2011.

2. Dense Error Correction via L1-Minimization,
   John Wright, and Yi Ma. IEEE Transactions on Information Theory, volume 56, no. 7, July 2010.

3. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization,
   John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng, and Yi Ma. In Proceedings of Neural Information Processing Systems (NIPS), December 2009.

4. Stable Principal Component Pursuit,
   Zihan Zhou, Xiaodong Li, John Wright, Emmanuel Candès, and Yi Ma. In Proceedings of IEEE International Symposium on Information Theory (ISIT), June 2010.

5. Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit,
   Arvind Ganesh, John Wright, Xiaodong Li, Emmanuel Candès, and Yi Ma. In Proceedings of IEEE International Symposium on Information Theory (ISIT), June 2010.

6. Principal Component Pursuit with Reduced Linear Measurements,
   Arvind Ganesh, Kerui Min, John Wright, and Yi Ma. submitted to International Symposium on Information Theory, 2012.

7. Compressive Principal Component Pursuit,
   John Wright, Arvind Ganesh, Kerui Min, and Yi Ma. submitted to International Symposium on Information Theory, 2012.

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