Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification

Tingting Ye; Chen Zu; Biao Jie; Dinggang Shen; Daoqiang Zhang

doi:10.1109/PRNI.2015.15

Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification

Tingting Ye, Chen Zu, Biao Jie, Dinggang Shen, Daoqiang Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	45-48
Number of pages	4
ISBN (Electronic)	9781467371452
DOIs	https://doi.org/10.1109/PRNI.2015.15
Publication status	Published - 2015 Sept 16
Event	5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 - Stanford, United States Duration: 2015 Jun 10 → 2015 Jun 12

Publication series

Name	Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015

Other

Other	5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Country/Territory	United States
City	Stanford
Period	15/6/10 → 15/6/12

Bibliographical note

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.

Keywords

Alzheimer's disease
discriminative regularization
group-sparsity regularizer
multi-modality based classification
multi-task feature selection

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/PRNI.2015.15

Cite this

Ye, T., Zu, C., Jie, B., Shen, D., & Zhang, D. (2015). Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. In Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 (pp. 45-48). Article 7270844 (Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRNI.2015.15

Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. / Ye, Tingting; Zu, Chen; Jie, Biao et al.
Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 45-48 7270844 (Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ye, T, Zu, C, Jie, B, Shen, D & Zhang, D 2015, Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. in Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015., 7270844, Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015, Institute of Electrical and Electronics Engineers Inc., pp. 45-48, 5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015, Stanford, United States, 15/6/10. https://doi.org/10.1109/PRNI.2015.15

Ye T, Zu C, Jie B, Shen D, Zhang D. Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. In Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 45-48. 7270844. (Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015). doi: 10.1109/PRNI.2015.15

Ye, Tingting ; Zu, Chen ; Jie, Biao et al. / Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 45-48 (Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015).

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title = "Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification",

abstract = "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.",

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author = "Tingting Ye and Chen Zu and Biao Jie and Dinggang Shen and Daoqiang Zhang",

note = "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: {\textcopyright} 2015 IEEE.; 5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 ; Conference date: 10-06-2015 Through 12-06-2015",

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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.

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Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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