Deep clustering via dual-supervised multi-kernel mapping

作     者：Ren, Lina Huang, Ruizhang Ma, Shengwei Qin, Yongbin Chen, Yanping Lin, Chuan 

作者机构：Guizhou Light Ind Tech Coll Dept Informat Engn Guiyang 550025 Peoples R China Guizhou Univ Coll Comp Sci & Technol Text Comp & Cognit Intelligence Engn Res Ctr Natl Educ MinistState Key Lab Publ Big Data Guiyang 550025 Peoples R China 

出 版 物：《PATTERN RECOGNITION》 (Pattern Recogn.)

年 卷 期：2025年第162卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Guizhou Privince Basic Research Program (Natural Science) General Project [zk General 338] Project of Guizhou Light Industry Technical College [24QY34] 

主　　题：Deep clustering Multiple kernel Kernel representation Semantic representation 

摘      要：In this paper, we propose a novel deep clustering framework via dual-supervised multi-kernel mapping, namely DCDMK, to improve clustering performance by learning linearly structural separable data representations. In the DCDMK framework, we introduce a kernel-aid encoder comprising two key components: a semantic representation learner, which captures the essential semantic information for clustering, and a multi-kernel representation learner, which dynamically selects the optimal combination of kernel functions through dual- supervised multi-kernel mapping to learn structurally separable kernel representations. The dual self-supervised mechanism is devised to jointly optimize both kernel representation learning and structural partitioning. Based on this framework, we introduce different fusion strategies to learn the multi-kernel representation of data samples for the clustering task. We derive two variants, namely DCDMK-WL (with layer-level kernel representation learning) and DCDMK-OL (without layer-level kernel representation learning). Extensive experiments on six real-world datasets demonstrate the effectiveness of our DCDMK framework.