Fine-grained multi-view clustering with robust multi-prototypes representation

作     者：Yin, Hongwei Wang, Guixiang Hu, Wenjun Zhang, Zhao 

作者机构：Huzhou Univ Sch Informat Engn & Zhejiang Prov Key Lab Smart Management Applicat Modern Agr Resources Huzhou 313000 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China 

出 版 物：《APPLIED INTELLIGENCE》 (Appl Intell)

年 卷 期：2023年第53卷第7期

页      面：8402-8420页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Zhejiang Basic Public Welfare Research Project [LGN18F020002] Natural Science Foundation of Zhejiang Province [LR20F020002] Anhui Provincial Natural Science Fund for Distinguished Young Scholars [2008085J30] Fundamental Research Funds for Central Universities of China [JZ2019HGPA0102] Huzhou Public Welfare Applied Research Project [2021GZ05] Huzhou University Graduate Scientific Research Innovation Project [2022KYCX44] Joint Funds of the National Natural Science Foundation of China [U20A20228] 

主　　题：Multi-view clustering Fine-grained fusion Multi-prototypes representation Sub-cluster structure 

摘      要：Multi-view clustering is a hot research topic that improves clustering performance by leveraging complementary information from multiple views. Recently, many multi-view clustering methods have been proposed. Most of them take the entire sample space as a fusion object and treat the local structures within each view equally. This paradigm is considered coarse-grained information fusion. However, in many real-world applications, different local structures with strong or weak clustering capacities could coexist in one view. To fully exploit valuable information of local structures, it is necessary to distinguish these local structures with different clustering capacities. In this paper, we propose a novel fine-grained multi-view clustering method. First, the sample space of each view is divided into many sub-clusters by using multi-prototypes representation. Second, the robustness of the multi-prototypes representation is enhanced by reducing the overlap between sub-clusters, which can reduce the effect of noise data. Finally, each sub-cluster s contribution weights are automatically assigned based on its clustering capacity. In addition, the robust multi-prototypes representation, the fine-grained multi-view fusion, and the clustering process are integrated into a unified framework. An effective alternating optimization algorithm is adopted to solve the objective function. Extensive experiments on two toy datasets and several real-world datasets prove that our method outperforms the traditional methods in clustering accuracy.