Enriched Robust Multi-View Kernel Subspace Clustering
This addresses the challenge of accurate clustering in multi-view datasets with non-linear structures, though it appears incremental as it builds on existing subspace clustering approaches.
The paper tackles the problem of subspace clustering by proposing a novel multi-view method that integrates affinity learning, multi-view fusion, and clustering into a single framework while handling non-linear data structures using kernels, achieving superior performance over state-of-the-art methods in experiments.
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.