Weighted Multi-View Spectral Clustering Based on Spectral Perturbation

Authors

  • Linlin Zong Dalian University of Technology
  • Xianchao Zhang Dalian University of Technology
  • Xinyue Liu Dalian University of Technology
  • Hong Yu Dalian University of Technology

Keywords:

Multi-view Clustering, Spectral Clusteing, Spectral Perturbation

Abstract

Considering the diversity of the views, assigning the multiviews with different weights is important to multi-view clustering. Several multi-view clustering algorithms have been proposed to assign different weights to the views. However, the existing weighting schemes do not simultaneously consider the characteristic of multi-view clustering and the characteristic of related single-view clustering. In this paper, based on the spectral perturbation theory of spectral clustering, we propose a weighted multi-view spectral clustering algorithm which employs the spectral perturbation to model the weights of the views. The proposed weighting scheme follows the two basic principles: 1) the clustering results on each view should be close to the consensus clustering result, and 2) views with similar clustering results should be assigned similar weights. According to spectral perturbation theory, the largest canonical angle is used to measure the difference between spectral clustering results. In this way, the weighting scheme can be formulated into a standard quadratic programming problem. Experimental results demonstrate the superiority of the proposed algorithm.

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Published

2018-04-29

How to Cite

Zong, L., Zhang, X., Liu, X., & Yu, H. (2018). Weighted Multi-View Spectral Clustering Based on Spectral Perturbation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11625