Enriched Robust Multi-View Kernel Subspace Clustering

TL;DR

This paper introduces a new multi-view kernel subspace clustering method that effectively handles non-linear data structures and integrates affinity learning, multi-view fusion, and clustering into a unified framework.

Contribution

It proposes an enriched robust multi-view kernel clustering framework with an iterative optimization approach, addressing limitations of existing two-stage linear methods.

Findings

Outperforms state-of-the-art clustering methods in experiments

Handles non-linear data structures effectively

Provides a simple, closed-form iterative optimization solution

Abstract

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.