Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering

TL;DR

This paper introduces a tensor-based intrinsic subspace learning method for multi-view clustering that effectively captures diverse statistical properties and high-order correlations across views, outperforming existing approaches.

Contribution

It proposes a novel tensor-based framework combining rank preserving decomposition and low-rank tensor constraints for improved multi-view clustering.

Findings

Outperforms existing multi-view clustering methods on nine datasets.

Effectively captures diverse statistical properties across views.

Utilizes tensor decomposition to mine high-order correlations.

Abstract

As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applications, since different views usually contain diverse statistic properties. To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation Learning (TISRL) for multi-view clustering in this paper. Concretely, the rank preserving decomposition is proposed firstly to effectively deal with the diverse statistic information contained in different views. Then, to achieve the intrinsic subspace representation, the tensor-singular value decomposition based low-rank tensor constraint is also utilized in our method. It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint. The objective function can be optimized by an augmented Lagrangian multiplier based alternating direction minimization algorithm. Experimental results on nine common used real-world multi-view datasets illustrate the superiority of TISRL.