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...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 35; no. 5; pp. 5076 - 5090
Main Authors Chen, Man-Sheng, Wang, Chang-Dong, Lai, Jian-Huang
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3151861