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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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 3; s. 1 - 14
Hlavní autoři: Huang, Shudong, Tsang, Ivor W., Xu, Zenglin, Lv, Jiancheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 diversity among views. To recover a clean graph, existing methods mainly focus on the diverse part within each graph yet overlook the diversity across multiple graphs. In this article, instead of merely considering the sparsity of diversity within a graph as previous methods do, we incline to a more suitable consideration that the diversity should be sparse across graphs. It is intuitive that the divergent parts are supposed to be inconsistent with each other, otherwise it would contradict the definition of diversity. By simultaneously and explicitly detecting the multiview consistency and cross-graph diversity, a pure graph for each view can be expected. The multiple pure graphs are further fused to the structured consensus graph with exactly <inline-formula> <tex-math notation="LaTeX">r</tex-math> </inline-formula> connected components where <inline-formula> <tex-math notation="LaTeX">r</tex-math> </inline-formula> is the number of clusters. Once the consensus graph is obtained, the cluster label to each instance can be directly allocated as each connected component precisely corresponds to an individual cluster. An alternating iterative algorithm is designed to optimize the subtasks of learning the similarity graphs adaptively, detecting the consistency as well as cross-graph diversity, fusing the multiple pure graphs, and assigning cluster label to each instance in a mutual reinforcement manner. Extensive experimental results on several benchmark multiview datasets demonstrate the effectiveness of our model, in comparison to several state-of-the-art algorithms.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3201964