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Multiple Empirical Kernel Learning (MEKL) explicitly maps the samples to empirical feature spaces, in which the feature vectors of the mapped samples are explicitly presented. Thus with the explicit representation of the samples, almost all algorithms can be kernelized directly, which is much easier in processing and analyzing the structure of the empirical feature spaces. However, in conventional...
Multiple empirical kernel learning (MEKL) is demonstrated to be flexible and effective due to introducing multiple kernels. But MEKL also brings a large computational complexity in practice. Therefore, in this paper we adopt the random projection (RP) technique to efficiently construct the low-dimensional feature space, and then develop an efficient and effective MEKL named MEKLRP so as to decrease...
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