Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

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123 Citations (Scopus)

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.

Original languageEnglish
Article number7185347
Pages (from-to)607-618
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number3
DOIs
Publication statusPublished - 2016 Mar 1

Keywords

  • Alzheimer's disease
  • feature selection
  • mild cognitive impairment
  • multi-class classification
  • neuroimaging data analysis
  • sparse coding
  • subspace learning

ASJC Scopus subject areas

  • Biomedical Engineering

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