Outlier-robust tensor PCA

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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書目詳細資料
Main Authors:	ZHOU, Pan, FENG, Jiashi
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2016
主題:	Artificial Intelligence and Robotics Graphics and Human Computer Interfaces
在線閱讀:	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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