  Multi-view clustering is an important yet challenging task due to the
difficulty of integrating the information from multiple representations. Most
existing multi-view clustering methods explore the heterogeneous information in
the space where the data points lie. Such common practice may cause significant
information loss because of unavoidable noise or inconsistency among views.
Since different views admit the same cluster structure, the natural space
should be all partitions. Orthogonal to existing techniques, in this paper, we
propose to leverage the multi-view information by fusing partitions.
Specifically, we align each partition to form a consensus cluster indicator
matrix through a distinct rotation matrix. Moreover, a weight is assigned for
each view to account for the clustering capacity differences of views. Finally,
the basic partitions, weights, and consensus clustering are jointly learned in
a unified framework. We demonstrate the effectiveness of our approach on
several real datasets, where significant improvement is found over other
state-of-the-art multi-view clustering methods.
