Open Access

ARTICLE

Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment

by Chuanli Wang¹, En Zhu¹, Xinwang Liu¹, Jiaohua Qin², Jianping Yin³, Kaikai Zhao⁴

1 College of Computer, National University of Defense Technology, Changsha, 410073, China.
2 College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China.
3 College of Computer, Dongguan University of Technology, Dongguan, 523000, China.
4 School of Informatics, Computing, and Engineering, Indiana University, Indiana, 47408, USA.

* Corresponding Author: Jianpin Yin. Email: .

Computers, Materials & Continua 2019, 61(1), 409-421. https://doi.org/10.32604/cmc.2019.06206

Abstract

Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample. However, we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance, while local kernel alignment on different sample actually has different contribution to clustering performance. Therefore this assumption could have a negative effective on clustering performance. To solve this issue, we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment, which can learn a proper weight to clustering performance for each local kernel alignment. Specifically, we introduce a new optimization variable- weight-to denote the contribution of each local kernel alignment to clustering performance, and then, weight, kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame. In addition, we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem. Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm. The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms, which illustrates the effectiveness of the proposed algorithm.

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Citations