  Kernel fusion is a popular and effective approach for combining multiple
features that characterize different aspects of data. Traditional approaches
for Multiple Kernel Learning (MKL) attempt to learn the parameters for
combining the kernels through sophisticated optimization procedures. In this
paper, we propose an alternative approach that creates dense embeddings for
data using the kernel similarities and adopts a deep neural network
architecture for fusing the embeddings. In order to improve the effectiveness
of this network, we introduce the kernel dropout regularization strategy
coupled with the use of an expanded set of composition kernels. Experiment
results on a real-world activity recognition dataset show that the proposed
architecture is effective in fusing kernels and achieves state-of-the-art
performance.
