Enhanced Multi-view Matrix Factorization with Shared Representation

Multi-view data is widely used in the real world, and traditional machine learning methods are not specifically designed for multi-view data. The goal of multi-view learning is to learn practical patterns from the divergent data sources. However, most previous researches focused on fitting feature embedding in target tasks, so researchers put forward with the algorithm which aims to learn appropriate patterns in data with associative properties. In this paper, a multi-view deep matrix factorization model is proposed for feature representation. First, the model constructs a multiple input neural network with shared hidden layers for finding a low-dimensional representation of all views. Second, the quality of representation matrix is evaluated using discriminators to improve the feature extraction capability of matrix factorization. Finally, the effectiveness of the proposed method is verified through comparative experiments on six real-world datasets.