Optimizing the data-dependent kernel under a unified kernel optimization framework
The kernel functions play a central role in kernel methods, accordingly over the years the optimization of kernel functions has been a promising research area. Ideally Fisher discriminant criteria can be used as an objective function to optimize the kernel function to augment the margin between different classes. Unfortunately, Fisher criteria are optimal only in the case that all the classes are generated from underlying multivariate normal distributions of common covariance matrix but different means and each class is expressed by a single cluster. Due to the assumptions, Fisher criteria obviously are not a suitable choice as a kernel optimization rule in some applications such as the multimodally distributed data. In order to solve this problem, recently many improved discriminant criteria (DC) have been also developed. Therefore, to apply these discriminant criteria to kernel optimization, in this paper based on a data-dependent kernel function we propose a unified kernel optimization framework, which can use any discriminant criteria formulated in a pairwise manner as the objective functions. Under the kernel optimization framework, to employ different discriminant criteria, one has to only change the corresponding affinity matrices without having to resort to any complex derivations in feature space. Experimental results based on some benchmark data demonstrate the efficiency of our method.
kernel optimization fisher criteria kernel machine kernel induced feature space