Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification
Year: 2015
Type: article
Abstract: 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.
Institutions: University of North Carolina at Chapel Hill, Korea University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: National Research Foundation, National Natural Science Foundation of China, Ministry of Science, ICT and Future Planning, National Research Foundation of Korea, National Institutes of Health, Institute for Information and Communications Technology Promotion, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering
Awards: G1234639804, G1583733560, G1600120695, G1610907132, Infant Brain Measurement and Super-Resolution Atlas Construction, G2686953907, G2784255609, G3139536118, G3258782064, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, G4798393751, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, G5487231792, Fast, Robust Analysis of Large Population Data, G5874234525, G6575006414, G6748413423, 稀疏学习模型的核心理论和技术研究, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G8854359148, G93323634
OpenAlex ID: W2286206973