Unifying complete and incomplete multi-view clustering through an information-theoretic generative model.

Autor: Zheng Y; School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangzhou, 510006, China. Electronic address: illusionzyh@foxmail.com., Zhou G; School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing, Ministry of Education, Guangzhou, 510006, China. Electronic address: gx.zhou@gdut.edu.cn., Huang H; School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; RIKEN AIP, Tokyo, Japan. Electronic address: libertyhhn@foxmail.com., Luo X; Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China; Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China. Electronic address: 2112204417@mail2.gdut.edu.cn., Huang Z; School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; 111 Center for Intelligent Batch Manufacturing Based on IoT Technology, Guangzhou, 510006, China. Electronic address: zhhuang.gdut@qq.com., Zhao Q; School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; RIKEN AIP, Tokyo, Japan. Electronic address: qibin.zhao@riken.jp.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2025 Feb; Vol. 182, pp. 106901. Date of Electronic Publication: 2024 Nov 22.
DOI: 10.1016/j.neunet.2024.106901
Abstrakt: Recently, Incomplete Multi-View Clustering (IMVC) has become a rapidly growing research topic, driven by the prevalent issue of incomplete data in real-world applications. Although many approaches have been proposed to address this challenge, most methods did not provide a clear explanation of the learning process for recovery. Moreover, most of them only considered the inter-view relationships, without taking into account the relationships between samples. The influence of irrelevant information is usually ignored, which has prevented them from achieving optimal performance. To tackle the aforementioned issues, we aim at unifying compLete and incOmplete multi-view clusterinG through an Information-theoretiC generative model (LOGIC). Specifically, we have defined three principles based on information theory: comprehensiveness, consensus, and compressibility. We first explain that the essence of learning to recover missing views is to maximize the mutual information between the common representation and the data from each view. Secondly, we leverage the consensus principle to maximize the mutual information between view distributions to uncover the associations between different samples. Finally, guided by the principle of compressibility, we remove as much task-irrelevant information as possible to ensure that the common representation effectively extracts semantic information. Furthermore, it can serve as a plug-and-play missing-data recovery module for multi-view clustering models. Through extensive empirical studies, we have demonstrated the effectiveness of our approach in generating missing views. In clustering tasks, our method consistently outperforms state-of-the-art (SOTA) techniques in terms of accuracy, normalized mutual information and purity, showcasing its superiority in both recovery and clustering performance.
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 © 2024. Published by Elsevier Ltd.)
Databáze: MEDLINE
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