TY - GEN
T1 - Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification
AU - Ye, Tingting
AU - Zu, Chen
AU - Jie, Biao
AU - Shen, Dinggang
AU - Zhang, Daoqiang
N1 - Funding Information:
This work is supported by NSFC (Nos.61422204, 61473149, 61473190), the Jiangsu SF for Distinguished Young Scholar (No.BK20130034), NUAA Fundamental Research Funds under Grant (No.NE2013105), Anhui Provincial NSF (No. 1508085MF125), the Open Projects Program of National Lab oratory of Pattern Recognition (No. 201407361) and NIH grants (EB006733, EB008374, EB009634, and AG041721).
Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/16
Y1 - 2015/9/16
N2 - Recently, multi-task based feature selection methods have been used in multi-modality based classification of Alzheimer's disease (AD) and its prodromal stage, i.e., Mild cognitive impairment (MCI). However, in traditional multi-task feature selection methods, some useful discriminative information among subjects is usually not well mined for further improving the subsequent classification performance. Accordingly, in this paper, we propose a discriminative multi-task feature selection method to select the most discriminative features for multi-modality based classification of AD/MCI. Specifically, for each modality, we traina linear regression model using the corresponding modality of data, and further enforce the group-sparsity regularization on weights of those regression models for joint selection of common features across multiple modalities. Furthermore, we propose a discriminative regularization term based on the intra-class and inter-class Laplacian matrices to better use the discriminative information among subjects. We perform extensive experiments on 202 subjects from the baseline MRI and FDG-PET image data of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method improves the classification performance with the comparison to several state-of the-art methods for multi-modality based AD/MCI classification.
AB - Recently, multi-task based feature selection methods have been used in multi-modality based classification of Alzheimer's disease (AD) and its prodromal stage, i.e., Mild cognitive impairment (MCI). However, in traditional multi-task feature selection methods, some useful discriminative information among subjects is usually not well mined for further improving the subsequent classification performance. Accordingly, in this paper, we propose a discriminative multi-task feature selection method to select the most discriminative features for multi-modality based classification of AD/MCI. Specifically, for each modality, we traina linear regression model using the corresponding modality of data, and further enforce the group-sparsity regularization on weights of those regression models for joint selection of common features across multiple modalities. Furthermore, we propose a discriminative regularization term based on the intra-class and inter-class Laplacian matrices to better use the discriminative information among subjects. We perform extensive experiments on 202 subjects from the baseline MRI and FDG-PET image data of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method improves the classification performance with the comparison to several state-of the-art methods for multi-modality based AD/MCI classification.
KW - Alzheimer's disease
KW - discriminative regularization
KW - group-sparsity regularizer
KW - multi-modality based classification
KW - multi-task feature selection
UR - http://www.scopus.com/inward/record.url?scp=84961825641&partnerID=8YFLogxK
U2 - 10.1109/PRNI.2015.15
DO - 10.1109/PRNI.2015.15
M3 - Conference contribution
AN - SCOPUS:84961825641
T3 - Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
SP - 45
EP - 48
BT - Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Y2 - 10 June 2015 through 12 June 2015
ER -