Outlier-robust tensor PCA
Low-rank tensor analysis is important for various real applications in computer vision. However, existing methods focus on recovering a low-rank tensor contaminated by Gaussian or gross sparse noise and hence cannot effectively handle outliers that are common in practical tensor data. To solve this...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/9008 https://ink.library.smu.edu.sg/context/sis_research/article/10011/viewcontent/2017_CVPR_RTPCA.pdf |
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| Institution: | Singapore Management University |
| Language: | English |