Tensor low-rank representation for data recovery and clustering

Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. This article develops a tensor low-rank representation (TLRR) method, which is the first approach that can exactly recover the clean data of intrinsic low-rank structure and a...

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Main Authors: ZHOU, Pan, LU, Canyi, FENG, Jiashi, LIN, Zhouchen, YAN, Shuicheng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/8997
https://ink.library.smu.edu.sg/context/sis_research/article/10000/viewcontent/2020_TPAMI_TLRR.pdf
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spelling sg-smu-ink.sis_research-100002024-07-25T08:20:16Z Tensor low-rank representation for data recovery and clustering ZHOU, Pan LU, Canyi FENG, Jiashi LIN, Zhouchen YAN, Shuicheng Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. This article develops a tensor low-rank representation (TLRR) method, which is the first approach that can exactly recover the clean data of intrinsic low-rank structure and accurately cluster them as well, with provable performance guarantees. In particular, for tensor data with arbitrary sparse corruptions, TLRR can exactly recover the clean data under mild conditions; meanwhile TLRR can exactly verify their true origin tensor subspaces and hence cluster them accurately. TLRR objective function can be optimized via efficient convex programing with convergence guarantees. Besides, we provide two simple yet effective dictionary construction methods, the simple TLRR (S-TLRR) and robust TLRR (R-TLRR), to handle slightly and severely corrupted data respectively. Experimental results on two computer vision data analysis tasks, image/video recovery and face clustering, clearly demonstrate the superior performance, efficiency and robustness of our developed method over state-of-the-arts including the popular LRR and SSC methods. 2021-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8997 info:doi/10.1109/TPAMI.2019.2954874 https://ink.library.smu.edu.sg/context/sis_research/article/10000/viewcontent/2020_TPAMI_TLRR.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Tensor low-rank representation low-rank tensor recovery tensor data clustering Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Tensor low-rank representation
low-rank tensor recovery
tensor data clustering
Databases and Information Systems
spellingShingle Tensor low-rank representation
low-rank tensor recovery
tensor data clustering
Databases and Information Systems
ZHOU, Pan
LU, Canyi
FENG, Jiashi
LIN, Zhouchen
YAN, Shuicheng
Tensor low-rank representation for data recovery and clustering
description Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. This article develops a tensor low-rank representation (TLRR) method, which is the first approach that can exactly recover the clean data of intrinsic low-rank structure and accurately cluster them as well, with provable performance guarantees. In particular, for tensor data with arbitrary sparse corruptions, TLRR can exactly recover the clean data under mild conditions; meanwhile TLRR can exactly verify their true origin tensor subspaces and hence cluster them accurately. TLRR objective function can be optimized via efficient convex programing with convergence guarantees. Besides, we provide two simple yet effective dictionary construction methods, the simple TLRR (S-TLRR) and robust TLRR (R-TLRR), to handle slightly and severely corrupted data respectively. Experimental results on two computer vision data analysis tasks, image/video recovery and face clustering, clearly demonstrate the superior performance, efficiency and robustness of our developed method over state-of-the-arts including the popular LRR and SSC methods.
format text
author ZHOU, Pan
LU, Canyi
FENG, Jiashi
LIN, Zhouchen
YAN, Shuicheng
author_facet ZHOU, Pan
LU, Canyi
FENG, Jiashi
LIN, Zhouchen
YAN, Shuicheng
author_sort ZHOU, Pan
title Tensor low-rank representation for data recovery and clustering
title_short Tensor low-rank representation for data recovery and clustering
title_full Tensor low-rank representation for data recovery and clustering
title_fullStr Tensor low-rank representation for data recovery and clustering
title_full_unstemmed Tensor low-rank representation for data recovery and clustering
title_sort tensor low-rank representation for data recovery and clustering
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/8997
https://ink.library.smu.edu.sg/context/sis_research/article/10000/viewcontent/2020_TPAMI_TLRR.pdf
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