Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

Research output: Contribution to journalArticle

99 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

Fingerprint

Neurodegenerative diseases
Neurodegenerative Diseases
Feature extraction
Learning
Neuroimaging
Alzheimer Disease
Discriminant analysis
Discriminant Analysis
Sample Size
Noise
Experiments

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

Cite this

@article{092e1c2b311d453882b9c851b748fd94,
title = "Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification",
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.",
keywords = "Alzheimer's disease, feature selection, mild cognitive impairment, multi-class classification, neuroimaging data analysis, sparse coding, subspace learning",
author = "Xiaofeng Zhu and Heung-Il Suk and Lee, {Seong Whan} and Dinggang Shen",
year = "2016",
month = "3",
day = "1",
doi = "10.1109/TBME.2015.2466616",
language = "English",
volume = "63",
pages = "607--618",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",
number = "3",

}

TY - JOUR

T1 - Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

AU - Zhu, Xiaofeng

AU - Suk, Heung-Il

AU - Lee, Seong Whan

AU - Shen, Dinggang

PY - 2016/3/1

Y1 - 2016/3/1

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - feature selection

KW - mild cognitive impairment

KW - multi-class classification

KW - neuroimaging data analysis

KW - sparse coding

KW - subspace learning

UR - http://www.scopus.com/inward/record.url?scp=84962091523&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962091523&partnerID=8YFLogxK

U2 - 10.1109/TBME.2015.2466616

DO - 10.1109/TBME.2015.2466616

M3 - Article

C2 - 26276982

AN - SCOPUS:84962091523

VL - 63

SP - 607

EP - 618

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 3

M1 - 7185347

ER -