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 |
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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 |
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Country | United States |
City | Stanford |
Period | 15/6/10 → 15/6/12 |
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