Clustering with similarity preserving

NEUROCOMPUTING

卷:365

Article

Kang, Zhao¹  Xu, Honghui¹  Wang, Boyu²  Zhu, Hongyuan³  Xu, Zenglin¹ 

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, SMILE Lab, Chengdu 611731, Sichuan, Peoples R China

[2] Univ Western Ontario, Dept Comp Sci, London, ON, Canada

[3] ASTAR, Inst Infocomm Res, Singapore, Singapore

关键词: Clustering;    Kernel method;    Similarity preserving;    Multiple kernel learning;    Graph learning;   

DOI  :  10.1016/j.neucom.2019.07.086

来源: Elsevier

【 摘 要 】

Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we require the learned graph be close to a kernel matrix, which serves as a measure of similarity in raw data. Moreover, the structure is adaptively tuned so that the number of connected components of the graph is exactly equal to the number of clusters. Finally, our method unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step. The effectiveness of this approach is examined on both single and multiple kernel learning scenarios in several datasets. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free