ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于邻域多核学习的后融合多视图聚类算法

1. 1(西南交通大学信息科学与技术学院 成都 611756);2(广西科技大学计算机科学与通信工程学院 广西柳州 545006) (vdx_swjtu@126.com)
• 出版日期: 2020-08-01
• 基金资助:
国家自然科学基金项目(61976247,61572407)

### Late Fusion Multi-View Clustering Based on Local Multi-Kernel Learning

Xia Dongxue1,2, Yang Yan1, Wang Hao1, Yang Shuhong2

1. 1(School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756);2(School of Computer Science and Communication Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi 545006)
• Online: 2020-08-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61976247, 61572407).

Abstract: Graph-based multi-view clustering is one of the representative methods in that field. However, existing models still have problems as following. First, most of them do not consider the difference of clustering capacity among different views and force all views to share a common similarity graph. Next, some models construct the similarity graph and conduct clustering in separated steps, resulting in the constructed similarity graph is not optimal for the following clustering tasks. Finally, although there are many models using kernel learning to deal with the nonlinear relationship between data points, most of them calculate the self-expressive relationship in kernel space based on global models. Such global schemes are not conducive to fully explore local nonlinear relationship, and easy to bring about heavy computing load. Therefore, this paper proposes a late fusion multi-view clustering model based on local multi-kernel learning. We implement information fusion at the level of class partition space rather than similarity graph, and adopt local multi-kernel learning scheme to fully preserve the local nonlinear relationship as well as reduce the computational load. We also propose an alternative optimization scheme to solve the construction of similarity graph, combination of multi-kernel and generation of class indicator matrix in a unified framework. Experiments on multiple datasets show that the proposed method has good multi-view clustering effect.