This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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106
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Type:
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Invited
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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International Association for Statistical Computing
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Abstract - #306277 |
Title:
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Robust Principal Component Analysis?
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Author(s):
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Emmanuel Candes*+ and Xiaodong Li and Yi Ma and John Wright
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Companies:
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Stanford University and Stanford University and Microsoft Research Asia and Microsoft Research Asia
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Address:
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, , CA, 94305, USA
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Keywords:
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Sparsity ;
Higher criticism ;
Random matrices ;
Likelihood ratios
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Abstract:
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This talk is about a curious phenomenon. Suppose we have a data matrix, which is the sum of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are corrupted. This extends to the situation where a fraction of the entries are missing as well. We present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition.
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