  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.
