Robust Deep Matrix Factorization with Low-rank and Hypergraph Learning for Multi-view Data Processing

As the advancement of sensing technology and cyber-physical equipment, the complexity and dimensionality of the data from consumer internet of things have increased dramatically. Processing the complex and multi-view data to analyze the consumption characteristics becomes a significant issue. Deep matrix factorization (DMF) is a widely spread method with the ability to learn intrinsic components in data based on its multiple-layers structures. However, the multiple-layers structures may magnify noises in data and the standard DMF cannot reflect the manifold and low-rank structures on the coefficient matrix well. In the paper, a robust deep matrix factorization with low-rank and hypergraph learning (RDMFLRH) model is proposed for multi-view data processing. Firstly, an arc tangent loss function with less sensitivity to noises and outlines is introduced to enhance robustness. Additionally, low-rank and hypergraph learning are adopted to extract the relationships between clusters and maintain the binary manifold structures from the original space. Then, an optimization algorithm based on the multiplicative update rule is developed to solve the proposed model with convergence proven theoretically and experimentally. Finally, abundant experiments on real-world and noisy datasets validate the superiority of the proposed method, improving around 1% to 10% performance compared with the comparison state-of-the-art methods.