IEEE J Biomed Health Inform - Multiple kernel learning in the primal for multimodal Alzheimer's disease classification.

Tópicos

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Resumo

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.

Resumo Limpo

achiev effect effici detect alzheim diseas ad mani machin learn method introduc realm howev general case limit train sampl well differ featur represent typic make problem challeng paper propos novel multipl kernellearn framework combin multimod featur ad classif scalabl easi implement contrari usual way solv problem dual look optim new perspect conduct fourier transform gaussian kernel explicit comput map function lead straightforward solut problem primal furthermor impos mix l norm constraint kernel weight known group lasso regular enforc group sparsiti among differ featur modal actual act role featur modal select time exploit complementari inform among differ kernel therefor abl extract discrimin featur classif experi adni dataset demonstr effect propos method

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