Sample-adaptive multiple kernel learning

Existing multiple kernel learning (MKL) algorithms indiscriminately

apply a same set of kernel combination weights to all samples. However,

the utility of base kernels could vary across samples and a base kernel

useful for one sample could become noisy for another. In this case, rigidly

applying a same set of kernel combination weights could adversely affect

the learning performance. To improve this situation, we propose a

sample-adaptive MKL algorithm, in which base kernels are allowed to

be adaptively switched on/off with respect to each sample. We achieve

this goal by assigning a latent binary variable to each base kernel when

it is applied to a sample. The kernel combination weights and the latent

variables are jointly optimized via margin maximization principle.

As demonstrated on five benchmark data sets, the proposed algorithm

consistently outperforms the comparable ones in the literature.