期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 63, 期 3, 页码 607-618出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2015.2466616
关键词
Alzheimer's disease; feature selection; mild cognitive impairment; multiclass classification; neuroimaging data analysis; sparse coding; subspace learning
资金
- NIH [EB006733, EB008374, EB009634, MH100217, AG041721, AG042599]
- ICT RAMP
- D Program of MSIP/IITP [Basic Software Research in Human-Level Lifelong Machine Learning (Machine Learning Centre)] [B0101-15-0307]
- National Research Foundation of Korea (NRF) grant - Korea government [NRF-2015R1A2A1A05001867]
- National Natural Science Foundation of China [61263035, 61573270]
- Ministry of Public Safety & Security (MPSS), Republic of Korea [B0101-15-0307] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2015R1C1A1A01052216, 2015R1A2A1A05001867] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods.
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