Enriched Robust Multi-View Kernel Subspace Clustering

Mengyuan Zhang, Kai Liu

Abstract:

Subspace clustering is to find underlying lowdimensional 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 multiview 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.