Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data

TL;DR

This paper introduces PSDMF, a semi-supervised deep matrix factorization model for multi-view data that effectively captures shared information while reducing uncorrelated noise, improving multi-view learning performance.

Contribution

The paper proposes a novel partially shared deep matrix factorization model that integrates graph regularization and semi-supervised regression for multi-view learning.

Findings

Outperforms state-of-the-art multi-view learning methods on benchmark datasets.

Effectively captures shared features and reduces uncorrelated information.

Provides an efficient iterative algorithm for model training.

Abstract

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.