NDLI: Multiple Kernel Learning in the Primal for Multimodal Alzheimer’s Disease Classification

Content Provider	IEEE Xplore Digital Library
Author	Fayao Liu Luping Zhou Chunhua Shen Jianping Yin
Copyright Year	2013
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.
Page Count	7
File Size	1240231
Starting Page	984
Ending Page	990
File Format	PDF
ISSN	21682194
Volume Number	18
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Magnetic resonance imaging Training Accuracy Fourier transforms Biomarkers random Fourier feature (RFF) Alzheimer’s disease (AD) group Lasso multimodal features multiple kernel learning (MKL)
Content Type	Text
Resource Type	Article
Subject	Health Information Management Health Informatics Electrical and Electronic Engineering Computer Science Applications Biotechnology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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