Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation

TL;DR

This paper introduces a novel structured tensor low-rank norm for multi-view spectral clustering, explicitly modeling intra-view and inter-view relationships, leading to superior clustering performance on benchmark datasets.

Contribution

The paper proposes a tailored tensor low-rank norm with symmetric and structured sparse constraints specifically designed for multi-view spectral clustering, improving upon existing methods.

Findings

Outperforms state-of-the-art methods significantly

Achieves perfect clustering on benchmark datasets

Demonstrates robustness and easy parameter tuning

Abstract

This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-rank norm without considering the special characteristics of the tensor in MVSC, we design a novel structured tensor low-rank norm tailored to MVSC. Specifically, we explicitly impose a symmetric low-rank constraint and a structured sparse low-rank constraint on the frontal and horizontal slices of the tensor to characterize the intra-view and inter-view relationships, respectively. Moreover, the two constraints could be jointly optimized to achieve mutual refinement. On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively. Extensive experimental results on five benchmark datasets show that the proposed method outperforms state-of-the-art methods to a significant extent. Impressively, our method is able to produce perfect clustering. In addition, the parameters of our method can be easily tuned, and the proposed model is robust to different datasets, demonstrating its potential in practice.