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

Journal Article


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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 work, we propose a novel multiple kernel learning framework to combine multi-modal 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 data set demonstrate the effectiveness of the proposed method.

Authors


  •   Liu, Fayao (external author)
  •   Zhou, Luping
  •   Shen, Chunhua (external author)
  •   Yin, Jianping (external author)

Publication Date


  • 2014

Citation


  • Liu, F., Zhou, L., Shen, C. & Yin, J. (2014). Multiple kernel learning in the primal for multimodal Alzheimer's disease classification. IEEE Journal of Biomedical and Health Informatics, 18 (3), 984-990.

Scopus Eid


  • 2-s2.0-84900841488

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4072&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3056

Has Global Citation Frequency


Number Of Pages


  • 6

Start Page


  • 984

End Page


  • 990

Volume


  • 18

Issue


  • 3

Place Of Publication


  • United States

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 work, we propose a novel multiple kernel learning framework to combine multi-modal 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 data set demonstrate the effectiveness of the proposed method.

Authors


  •   Liu, Fayao (external author)
  •   Zhou, Luping
  •   Shen, Chunhua (external author)
  •   Yin, Jianping (external author)

Publication Date


  • 2014

Citation


  • Liu, F., Zhou, L., Shen, C. & Yin, J. (2014). Multiple kernel learning in the primal for multimodal Alzheimer's disease classification. IEEE Journal of Biomedical and Health Informatics, 18 (3), 984-990.

Scopus Eid


  • 2-s2.0-84900841488

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4072&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3056

Has Global Citation Frequency


Number Of Pages


  • 6

Start Page


  • 984

End Page


  • 990

Volume


  • 18

Issue


  • 3

Place Of Publication


  • United States