Incomplete Multi-view Data Learning via Adaptive Embedding and Partial l 2,1 Norm Constraints for Parkinson's Disease Diagnosis

Huang, Zhongwei; Wang, Kai; Chen, Chao; Chen, Jianxia; Wan, Jun; Yang, Zhi; Zhou, Ran; Gan, Haitao

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by mental abnormalities and motor dysfunction. Its early classification and prediction of clinical scores have been major concerns for researchers. Currently, multi-view data learning has become an essential research area due to the capacity of multiple views to provide complementary insights from various perspectives. However, the discontinuous distribution, data missing complexity, small sample size, and redundant features in multi-view datasets pose a substantial obstacle, and most existing multi-view learning methods are unable to handle these challenges effectively. In this study, we propose a novel incomplete multi-view data learning framework (IMVDL) via dynamic embedding and partial l_(2,1) norm constraints for PD diagnosis. Specifically, multi-view dynamic embedding can adapt to any view missing scene, thereby linearly/nonlinearly mapping incomplete multi-view data to low-dimensional manifold spaces and generating complete multi-view data representations. The partial l_(2,1) norm constraint can ignore larger feature weight values and perform l_(2,1) norm sparse on the remaining weights, thereby avoiding the sparse bias problem caused by larger weight values. An efficient iterative algorithm is derived to find the optimal solution of the IMVDL method. We conduct extensive experiments using multi-modal neuroimage data from the Parkinson’s Progression Markers Initiative (PPMI) database. The results demonstrate that the IMVDL method is superior to other comparative methods. The source code for IMVDL is available at https://github.com/a610lab/IMVDL/.

Citation

Huang, Z., Wang, K., Chen, C., Chen, J., Wan, J., Yang, Z., Zhou, R., & Gan, H. (in press). Incomplete Multi-view Data Learning via Adaptive Embedding and Partial l 2,1 Norm Constraints for Parkinson's Disease Diagnosis. IEEE Journal of Biomedical and Health Informatics,

• Journal Article Type: Article
• Acceptance Date: Jun 3, 2025
• Deposit Date: Jun 3, 2025
• Journal: IEEE Journal of Biomedical and Health Informatics
• Electronic ISSN: 2168-2194
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/49890858
• Publisher URL: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221020