Self-Weighting and Hypergraph Regularization for Multi-view Spectral Clustering

Abstract

Leveraging the consensus and complementary principle to find a common representation for different views is an essential problem of multi-view clustering. To address the problem, many Low-Rank Representation (LRR) based methods have been proposed. However, existing LRR based methods have two common limitations: 1) they adopt graph regularization that only considers simple pairwise similarities among data points, and 2) they do not generally characterize the importance of each view. In this letter, we correspondingly utilize hypergraph regularization and a self-weighting strategy to handle the limitations with an LRR based model. Specifically, in our model, we construct hypergraph Laplacian matrices of each view that explicitly contain high order relations among data points, to improve the usage of complementary information. Meanwhile, the self-weighting strategy that preserves view specific information and assigns adaptive weights to each view is leveraged to take full advantage of multi-view consensus information. Based on the Augmented Lagrangian Multiplier (ALM) scheme, we design an effective alternating iterative strategy to optimize the model. Extensive experiments conducted on four benchmark datasets validate the superiority of our method.