Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data

• Published in: IEEE transaction on neural networks and learning systems 2021-04, Vol.32 (4), p.1486-1496
• Main Authors: Zhao, Liang, Yang, Tao, Zhang, Jie, Chen, Zhikui, Yang, Yi, Wang, Z. Jane
• Format: Article
• Language: English
• Subjects: Co-learning; correlated features; Correlation; Data analysis; Data models; Datasets; Encoding; Learning; Learning systems; multi-view data; Objective function; Optimization; Semantics; Transforms; uncorrelated features
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2020.2984810

Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifically, view-specific (uncorrelated) features are identified for each view when learning the common (correlated) feature across views in the latent semantic subspace. By eliminating the effects of uncorrelated information, useful inter-view feature correlations can be captured. We design a new objective function in CoUFC and derive an optimization approach to solve the objective with the analysis on its convergence. Experiments on real-world sensor, image, and text data sets demonstrate that the proposed method outperforms the state-of-the-art multiview learning methods.