Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
This work provides an incremental improvement for researchers and practitioners working with multi-view learning and deep matrix factorization.
This paper addresses the challenge of integrating view-specific features and label information into deep multi-view matrix factorization. The proposed Partially Shared Semi-supervised Deep Matrix Factorization model (PSDMF) learns a compact and discriminative representation by eliminating uncorrelated information, outperforming state-of-the-art multi-view learning approaches on five benchmark datasets.
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.