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 (Print) | 9781467371452 |
| DOIs | |
| Publication status | Published - 2015 Sep 16 |
| Externally published | Yes |
| Event | 5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 - Stanford, United States Duration: 2015 Jun 10 → 2015 Jun 12 |
Other
| Other | 5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 |
|---|---|
| Country | United States |
| City | Stanford |
| Period | 15/6/10 → 15/6/12 |
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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
Cite this
Discriminative Multi-task Feature Selection for Multi-modality Based AD/MCI Classification. / Ye, Tingting; Zu, Chen; Jie, Biao; Shen, Dinggang; Zhang, Daoqiang.
Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 45-48 7270844.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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
UR - http://www.scopus.com/inward/citedby.url?scp=84961825641&partnerID=8YFLogxK
U2 - 10.1109/PRNI.2015.15
DO - 10.1109/PRNI.2015.15
M3 - Conference contribution
AN - SCOPUS:84961825641
SN - 9781467371452
SP - 45
EP - 48
BT - Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -