  Multi-view clustering is an important approach to analyze multi-view data in
an unsupervised way. Among various methods, the multi-view subspace clustering
approach has gained increasing attention due to its encouraging performance.
Basically, it integrates multi-view information into graphs, which are then fed
into spectral clustering algorithm for final result. However, its performance
may degrade due to noises existing in each individual view or inconsistency
between heterogeneous features. Orthogonal to current work, we propose to fuse
multi-view information in a partition space, which enhances the robustness of
Multi-view clustering. Specifically, we generate multiple partitions and
integrate them to find the shared partition. The proposed model unifies graph
learning, generation of basic partitions, and view weight learning. These three
components co-evolve towards better quality outputs. We have conducted
comprehensive experiments on benchmark datasets and our empirical results
verify the effectiveness and robustness of our approach.
