Abstract
In this paper, we study robust principal component analysis on tensors, in the setting where frame-wise outliers exist. We propose a convex formulation to decompose a tensor into a low rank component and a frame-wise sparse component. Theoretically, we guarantee that exact subspace recovery and outlier identification can be achieved under mild model assumptions. Compared with entry-wise outlier pursuit and naive matricization of tensors with frame-wise outliers, our approach can handle higher ranks and proportion of outliers. Extensive numerical evaluations are provided on both synthetic and real data to support our theory.
Original language | English (US) |
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Title of host publication | Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 |
Publisher | IEEE Computer Society |
Pages | 1744-1749 |
Number of pages | 6 |
ISBN (Electronic) | 9781538639542 |
DOIs | |
State | Published - Mar 1 2017 |
Event | 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016 |
Other
Other | 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/6/16 → 11/9/16 |