Multi-view subspace clustering via adaptive graph learning and late fusion alignment.

Autor: Tang C; School of Computer Science, China University of Geosciences, No. 68 Jincheng Road, 430078, Wuhan, China. Electronic address: tangchuan@cug.edu.cn., Sun K; School of Computer Science, China University of Geosciences, No. 68 Jincheng Road, 430078, Wuhan, China. Electronic address: sunkun@cug.edu.cn., Tang C; School of Computer Science, China University of Geosciences, No. 68 Jincheng Road, 430078, Wuhan, China. Electronic address: tangchang@cug.edu.cn., Zheng X; School of Computer, National University of Defense Technology, Deya Road, 410073, Changsha, China. Electronic address: zhengxiao@nudt.edu.cn., Liu X; School of Computer, National University of Defense Technology, Deya Road, 410073, Changsha, China. Electronic address: xinwangliu@nudt.edu.cn., Huang JJ; School of Computer, National University of Defense Technology, Deya Road, 410073, Changsha, China. Electronic address: jjhuang@nudt.edu.cn., Zhang W; Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputing Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), 250000, Jinan, China. Electronic address: wzhang@qlu.edu.cn.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2023 Aug; Vol. 165, pp. 333-343. Date of Electronic Publication: 2023 Jun 03.
DOI: 10.1016/j.neunet.2023.05.019
Abstrakt: Multi-view subspace clustering has attracted great attention due to its ability to explore data structure by utilizing complementary information from different views. Most of existing methods learn a sample representation coefficient matrix or an affinity graph for each single view, then the final clustering result is obtained from the spectral embedding of a consensus graph using certain traditional clustering techniques, such as k-means. However, clustering performance will be degenerated if the early fusion of partitions cannot fully exploit relationships between all samples. Different from existing methods, we propose a multi-view subspace clustering method via adaptive graph learning and late fusion alignment (AGLLFA). For each view, AGLLFA learns an affinity graph adaptively to capture the similarity relationship among samples. Moreover, a spectral embedding learning term is designed to exploit the latent feature space of different views. Furthermore, we design a late fusion alignment mechanism to generate an optimal clustering partition by fusing view-specific partitions obtained from multiple views. An alternate updating algorithm with validated convergence is developed to solve the resultant optimization problem. Extensive experiments on several benchmark datasets are conducted to illustrate the effectiveness of the proposed method when compared with other state-of-the-art methods. The demo code of this work is publicly available at https://github.com/tangchuan2000/AGLLFA.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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