Multi-Modal Feature Selection with Feature Correlation and Feature Structure Fusion for MCI and AD Classification

• Published in: Brain sciences Vol. 12; no. 1; p. 80
• Main Authors: Jiao, Zhuqing, Chen, Siwei, Shi, Haifeng, Xu, Jia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.01.2022; MDPI
• Subjects: Algorithms; Alzheimer's disease; Brain research; Classification; Cognitive ability; Complementarity; feature correlation; Feature selection; feature structure fusion; Graphs; Information sharing; Magnetic resonance imaging; Memory; multi-modal; Neurodegenerative diseases; Nodes; Tomography; classification; multi-modal; feature correlation; feature selection; feature structure fusion
• DOI: 10.3390/brainsci12010080
• ISSN: 2076-3425

Feature selection for multiple types of data has been widely applied in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) classification research. Combining multi-modal data for classification can better realize the complementarity of valuable information. In order to improve the classification performance of feature selection on multi-modal data, we propose a multi-modal feature selection algorithm using feature correlation and feature structure fusion (FC2FS). First, we construct feature correlation regularization by fusing a similarity matrix between multi-modal feature nodes. Then, based on manifold learning, we employ feature matrix fusion to construct feature structure regularization, and learn the local geometric structure of the feature nodes. Finally, the two regularizations are embedded in a multi-task learning model that introduces low-rank constraint, the multi-modal features are selected, and the final features are linearly fused and input into a support vector machine (SVM) for classification. Different controlled experiments were set to verify the validity of the proposed method, which was applied to MCI and AD classification. The accuracy of normal controls versus Alzheimer’s disease, normal controls versus late mild cognitive impairment, normal controls versus early mild cognitive impairment, and early mild cognitive impairment versus late mild cognitive impairment achieve 91.85 ± 1.42%, 85.33 ± 2.22%, 78.29 ± 2.20%, and 77.67 ± 1.65%, respectively. This method makes up for the shortcomings of the traditional multi-modal feature selection based on subjects and fully considers the relationship between feature nodes and the local geometric structure of feature space. Our study not only enhances the interpretation of feature selection but also improves the classification performance, which has certain reference values for the identification of MCI and AD.