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In this paper we propose solving localized multiple kernel learning (LMKL)using LMKL-Net, a feedforward deep neural network. In contrast to previousworks, as a learning principle we propose {\em parameterizing} both the gatingfunction for learning kernel combination weights and the multiclass classifierin LMKL using an attentional network (AN) and a multilayer perceptron (MLP),respectively. In this way we can learn the (nonlinear) decision function inLMKL (approximately) by sequential applications of AN and MLP. Empirically onbenchmark datasets we demonstrate that overall LMKL-Net can not only outperformthe state-of-the-art MKL solvers in terms of accuracy, but also be trainedabout {\em two orders of magnitude} faster with much smaller memory footprintfor large-scale learning.

LMKL-Net: A Fast Localized Multiple Kernel Learning Solver via Deep Neural Networks