Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering

Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously dec...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 32; H. 1; S. 92 - 104
Hauptverfasser:	Chen, Yongyong, Xiao, Xiaolin, Peng, Chong, Lu, Guangming, Zhou, Yicong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Affinity Algorithms Clustering Clustering algorithms Clustering methods Correlation graph learning Learning low-rank Matrix decomposition Multi-view clustering Optimization Outliers (statistics) Representations Sparse matrices Subspace methods Subspaces tensor approximation Tensors
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
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