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 language | English |
|---|---|
| Article number | 7185347 |
| Pages (from-to) | 607-618 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 63 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2016 Mar 1 |
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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
Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification. / Zhu, Xiaofeng; Suk, Heung-Il; Lee, Seong Whan; Shen, Dinggang.
In: IEEE Transactions on Biomedical Engineering, Vol. 63, No. 3, 7185347, 01.03.2016, p. 607-618.Research output: Contribution to journal › Article
}
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
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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 -