Kernel‐based low‐rank tensorized multiview spectral clustering

Abstract

Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high-dimensional data, whose noise goes against the clustering results. Besides, some methods based on self-representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel-based Low-rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high-dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low-rank tensor is learned in the process of exploring the high-order correlations between data. Experiments on real-world data sets show that our method not only yields better results but also is quite time-saving compared with other state-of-the-art models.