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.

本研究提出了一种新颖的多视图子空间聚类方法，其中采用了 Enriched Robust Multi-View Kernel Subspace Clustering 框架，结合多视角数据和谱聚类学习一致性亲和矩阵，解决了现有方法采用两阶段框架以及假设域数据均为线性子空间的问题。实验证明，该方法表现优于现有的聚类方法。