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Fast correntropy-based multi-view clustering with prototype graph factorization.
- Source :
-
Information Sciences . Oct2024, Vol. 681, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- As a consequence of the ability to incorporate information from different perspectives, multi-view clustering has gained significant attention. Nevertheless, 1) its high computational cost, particularly when processing large-scale and high-dimensional multi-view data, restricts its applications in practice; and 2) complex noise in real-world data also challenges the robustness of existing algorithms. To tackle the above challenges, we develop a fast correntropy-based multi-view clustering algorithm with prototype graph factorization (FCMCPF). FCMCPF first adopts prototype graphs to effectively mitigate the complexity associated with graph construction, thereby reducing it from a quadratic complexity to a linear one. Then, it decomposes these prototype graphs under the correntropy criterion to robustly find the cluster indicator matrix without any post-processing. To solve the non-convex and non-linear model, we devise a fast half-quadratic-based strategy to first convert it into a convex formulation and then swiftly complete the optimization via the matrix properties of orthogonality and trace. The extensive experiments conducted on noisy and real-world datasets illustrate that FCMCPF is highly efficient and robust compared to other advanced algorithms, with comparable or even superior clustering effectiveness. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GRAPH algorithms
*FACTORIZATION
*PROTOTYPES
*ALGORITHMS
*NOISE
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 681
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 178885147
- Full Text :
- https://doi.org/10.1016/j.ins.2024.121256