ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease. (June 2022)

• Record Type: Journal Article
• Authors: Shi, Yuang; Zu, Chen; Hong, Mei; Zhou, Luping; Wang, Lei; Wu, Xi; Zhou, Jiliu; Zhang, Daoqiang; Wang, Yan
• Is Part Of: Pattern recognition. Volume 126(2022)
• Publication Date: 2022-06
• Subjects: Multi-modality -- Similarity learning -- Feature selection -- Alzheimer's disease; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108566
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: An adaptive-similarity-based multi-modality feature selection method is proposed. This method captures the intrinsic similarity shared across different modality data. This method discovers the disease sensitive biomarkers. This method yields better performance on identifying disease status. Abstract: Multimodal classification methods using different modalities have great advantages over traditional single-modality-based ones for the diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). With the increasing amount of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become a crucial research direction for AD classification. However, traditional methods usually depict the data structure using pre-defined similarity matrix as a priori, which is difficult to precisely measure the intrinsic relationship across different modalities in high-dimensional space. In this paper, we propose a novel multimodal feature selection method called Adaptive-Similarity-based Multi-modality Feature Selection (ASMFS) which performs adaptive similarity learning and feature selection simultaneously. Specifically, a similarity matrix is learned by jointly considering different modalities and at the same time, an efficient feature selection is conducted by imposing group sparsity-inducing l 2, 1 -norm constraint. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline MRI and FDG-PET imaging data collected from 51 AD, 43 MCI converters (MCI-C), 56 MCI non-converters (MCI-NC) and 52 normal controls (NC), we demonstrate the effectiveness and superiority of our proposed method against other state-of-the-art approaches for multi-modality classification of AD/MCI.