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https://hdl.handle.net/2440/86763
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Type: | Journal article |
Title: | Multiple kernel learning in the primal for multimodal Alzheimer's disease classification |
Author: | Liu, F. Zhou, L. Shen, C. Yin, J. |
Citation: | IEEE Journal of Biomedical and Health Informatics, 2014; 18(3):984-990 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2014 |
ISSN: | 2168-2194 2168-2208 |
Statement of Responsibility: | Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin |
Abstract: | To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this paper, we propose a novel multiple kernel-learning framework to combine multimodal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which leads to a more straightforward solution of the problem in the primal. Furthermore, we impose the mixed L21 norm constraint on the kernel weights, known as the group lasso regularization, to enforce group sparsity among different feature modalities. This actually acts as a role of feature modality selection, while at the same time exploiting complementary information among different kernels. Therefore, it is able to extract the most discriminative features for classification. Experiments on the ADNI dataset demonstrate the effectiveness of the proposed method. |
Keywords: | Alzheimer’s disease (AD); group Lasso; multimodal features; multiple kernel learning (MKL); random Fourier feature (RFF) |
Rights: | © 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/JBHI.2013.2285378 |
Published version: | http://dx.doi.org/10.1109/jbhi.2013.2285378 |
Appears in Collections: | Aurora harvest 2 Computer Science publications |
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