Dual Representation Learning for One-Step Clustering of Multi-View Data

Understand

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.