  Multiple kernel learning (MKL) algorithms combine different base kernels to
obtain a more efficient representation in the feature space. Focusing on
discriminative tasks, MKL has been used successfully for feature selection and
finding the significant modalities of the data. In such applications, each base
kernel represents one dimension of the data or is derived from one specific
descriptor. Therefore, MKL finds an optimal weighting scheme for the given
kernels to increase the classification accuracy. Nevertheless, the majority of
the works in this area focus on only binary classification problems or aim for
linear separation of the classes in the kernel space, which are not realistic
assumptions for many real-world problems. In this paper, we propose a novel
multi-class MKL framework which improves the state-of-the-art by enhancing the
local separation of the classes in the feature space. Besides, by using a
sparsity term, our large-margin multiple kernel algorithm (LMMK) performs
discriminative feature selection by aiming to employ a small subset of the base
kernels. Based on our empirical evaluations on different real-world datasets,
LMMK provides a competitive classification accuracy compared with the
state-of-the-art algorithms in MKL. Additionally, it learns a sparse set of
non-zero kernel weights which leads to a more interpretable feature selection
and representation learning.
