%%% -*-BibTeX-*-
@article{Liu2014MKL,
    author = {Fayao Liu  and  Luping Zhou  and  Chunhua Shen  and  Jianping Yin},
    title  = {Multiple Kernel Learning in the Primal for Multi-modal {A}lzheimer's Disease Classification},
    journal= {IEEE Journal of Biomedical and Health Informatics},
    volume = {},
    number = {},
    year   = {2014},
    month  = {},
    pages  = {},
    eprint = {1310.0890},
    url    = {http://dx.doi.org/10.1109/JBHI.2013.2285378},
    venue  = {JBHI},
    note   = {Online published at IEEE: 10 October 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 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 space, 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 space. Furthermore, we impose the
             mixed $L_{21}$ 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.
    },
}