  Multi-view clustering aims at integrating complementary information from
multiple heterogeneous views to improve clustering results. Existing multi-view
clustering solutions can only output a single clustering of the data. Due to
their multiplicity, multi-view data, can have different groupings that are
reasonable and interesting from different perspectives. However, how to find
multiple, meaningful, and diverse clustering results from multi-view data is
still a rarely studied and challenging topic in multi-view clustering and
multiple clusterings. In this paper, we introduce a deep matrix factorization
based solution (DMClusts) to discover multiple clusterings. DMClusts gradually
factorizes multi-view data matrices into representational subspaces
layer-by-layer and generates one clustering in each layer. To enforce the
diversity between generated clusterings, it minimizes a new redundancy
quantification term derived from the proximity between samples in these
subspaces. We further introduce an iterative optimization procedure to
simultaneously seek multiple clusterings with quality and diversity.
Experimental results on benchmark datasets confirm that DMClusts outperforms
state-of-the-art multiple clustering solutions.
