  Multi-view clustering (MVC) has been extensively studied to collect multiple
source information in recent years. One typical type of MVC methods is based on
matrix factorization to effectively perform dimension reduction and clustering.
However, the existing approaches can be further improved with following
considerations: i) The current one-layer matrix factorization framework cannot
fully exploit the useful data representations. ii) Most algorithms only focus
on the shared information while ignore the view-specific structure leading to
suboptimal solutions. iii) The partition level information has not been
utilized in existing work. To solve the above issues, we propose a novel
multi-view clustering algorithm via deep matrix decomposition and partition
alignment. To be specific, the partition representations of each view are
obtained through deep matrix decomposition, and then are jointly utilized with
the optimal partition representation for fusing multi-view information.
Finally, an alternating optimization algorithm is developed to solve the
optimization problem with proven convergence. The comprehensive experimental
results conducted on six benchmark multi-view datasets clearly demonstrates the
effectiveness of the proposed algorithm against the SOTA methods.
