Class-Specific Discriminability and Multiscale Information-Based Multiview Feature Selection

Biao Xiang, Hongmei Chen, Yong Mi, Binbin Sang, Shi-Jinn Horng, Tianrui Li

Abstract: Multiview data possess different discriminability in different views, which is challenging to catch but crucial for a feature selection model. Multiscale information, which represents vertical exploration in each view, is vital for further mining traits implied in multiview data. However, most existing studies neglect these beneficial multi-granulation characteristics. This study first embeds the multiscale information into the sparse learning framework for multiview feature selection. A class-specific discriminability and multiscale information-based multiview feature selection (CDMIMFS) method is proposed. It explores the fuzzy and uncertain class-specific discriminability which is inherently discrepant in different views by the fuzzy rough set theory. It relaxes the over strict requirement for complete consistency in multiscale information systems to make a trade-off, which further enhances discriminative feature selection. An effective iteration algorithm is proposed to solve the optimization. Both the theoretical proof and experimental demonstration of convergence are provided. Comprehensive experiments are conducted on the CDMIMFS compared with state-of-the-art algorithms. Results on different evaluation metrics exhibit the advantages of the proposed method.

External IDs:doi:10.1109/tcsvt.2025.3576250