CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection

Multiview graph clustering has emerged as an important yet challenging technique due to the difficulty of exploiting the similarity relationships among multiple views. Typically, the similarity graph for each view learned by these methods is easily corrupted because of the unavoidable noise or diver...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 35; no. 3; pp. 1 - 14
Main Authors:	Huang, Shudong, Tsang, Ivor W., Xu, Zenglin, Lv, Jiancheng
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Algorithms Benchmark testing Clustering Clustering algorithms Consistency Correlation Cross-graph diversity detection (CGDD) graph learning Graphs Iterative algorithms Iterative methods Kernel Labels Learning systems multigraph fusion multiview clustering Similarity Sparse matrices
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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