Low-Rank Tensor Based Proximity Learning for Multi-View Clustering

Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems. (1) They target at studying a common representation or pairwise c...

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Vydané v:IEEE transactions on knowledge and data engineering Ročník 35; číslo 5; s. 5076 - 5090
Hlavní autori: Chen, Man-Sheng, Wang, Chang-Dong, Lai, Jian-Huang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
adaptive confidences
Affinity
Clustering
Clustering methods
consensus indicator
Correlation
Data structures
Kernel
Knowledge representation
Learning
low-rank tensor representation
Mathematical analysis
Multi-view clustering
Semantics
Sparse matrices
Tensors
ISSN:1041-4347, 1558-2191
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Shrnutí:Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems. (1) They target at studying a common representation or pairwise correlations between views, neglecting the comprehensiveness and deeper higher-order correlations among multiple views. (2) The prior knowledge of view-specific representation can not be taken into account to obtain the consensus indicator graph in a unified graph construction and clustering framework. To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework. Specifically, multiple affinity representations are stacked in a low-rank constrained tensor to recover their comprehensiveness and higher-order correlations. Meanwhile, view-specific representation carrying different adaptive confidences is jointly linked with the consensus indicator graph. Extensive experiments on nine real-world datasets indicate the superiority of LTBPL compared with the state-of-the-art methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3151861