  Since many real-world data can be described from multiple views, multi-view
learning has attracted considerable attention. Various methods have been
proposed and successfully applied to multi-view learning, typically based on
matrix factorization models. Recently, it is extended to the deep structure to
exploit the hierarchical information of multi-view data, but the view-specific
features and the label information are seldom considered. To address these
concerns, we present a partially shared semi-supervised deep matrix
factorization model (PSDMF). By integrating the partially shared deep
decomposition structure, graph regularization and the semi-supervised
regression model, PSDMF can learn a compact and discriminative representation
through eliminating the effects of uncorrelated information. In addition, we
develop an efficient iterative updating algorithm for PSDMF. Extensive
experiments on five benchmark datasets demonstrate that PSDMF can achieve
better performance than the state-of-the-art multi-view learning approaches.
The MATLAB source code is available at
https://github.com/libertyhhn/PartiallySharedDMF.
