  Multi-view data are commonly encountered in data mining applications.
Effective extraction of information from multi-view data requires specific
design of clustering methods to cater for data with multiple views, which is
non-trivial and challenging. In this paper, we propose a novel one-step
multi-view clustering method by exploiting the dual representation of both the
common and specific information of different views. The motivation originates
from the rationale that multi-view data contain not only the consistent
knowledge between views but also the unique knowledge of each view. Meanwhile,
to make the representation learning more specific to the clustering task, a
one-step learning framework is proposed to integrate representation learning
and clustering partition as a whole. With this framework, the representation
learning and clustering partition mutually benefit each other, which
effectively improve the clustering performance. Results from extensive
experiments conducted on benchmark multi-view datasets clearly demonstrate the
superiority of the proposed method.
