Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually p...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 8; s. 11436 - 11447
Hlavní autori: Fang, Si-Guo, Huang, Dong, Cai, Xiao-Sha, Wang, Chang-Dong, He, Chaobo, Tang, Yong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2024
Predmet:
Bipartite graph
Bipartite graph learning
Clustering algorithms
data clustering
Fuses
Laplace equations
large-scale clustering
Linear programming
linear time
multi-view clustering (MVC)
Optimization
Partitioning algorithms
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via <inline-formula> <tex-math notation="LaTeX">\boldsymbol {u} </tex-math></inline-formula>nified and <inline-formula> <tex-math notation="LaTeX">\boldsymbol {d} </tex-math></inline-formula>iscrete <inline-formula> <tex-math notation="LaTeX">\boldsymbol {b} </tex-math></inline-formula>ipartite <inline-formula> <tex-math notation="LaTeX">\boldsymbol {g} </tex-math></inline-formula>raph <inline-formula> <tex-math notation="LaTeX">\boldsymbol {l} </tex-math></inline-formula>earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL .
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3261460