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Multiple kernel learning in the primal for multimodal Alzheimer's disease classification.

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.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Liu F,Zhou L,Shen C,Yin J

doi

10.1109/JBHI.2013.2285378

subject

Has Abstract

pub_date

2014-05-01 00:00:00

pages

984-90

issue

3

eissn

2168-2194

issn

2168-2208

journal_volume

18

pub_type

杂志文章

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