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.