期刊
BRAIN IMAGING AND BEHAVIOR
卷 9, 期 4, 页码 913-926出版社
SPRINGER
DOI: 10.1007/s11682-015-9356-x
关键词
Mild cognitive impairment conversion; Manifold regularization; Transfer learning; Semi-supervised learning; Multimodal classification; Sample selection
类别
资金
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- National Natural Science Foundation of China [61422204, 61473149, 61473190, 1401271, 81471733]
- Jiangsu Natural Science Foundation for Distinguished Young Scholar [BK20130034]
- Specialized Research Fund for the Doctoral Program of Higher Education [20123218110009]
- NUAA Fundamental Research Funds [NE2013105]
- NIH [EB006733, EB008374, EB009634, MH100217, AG041721, AG042599]
As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.
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