Distance metric learning with kernels
In this paper, we propose a feature weighting method that works in both the input space and the kernel-induced feature space. It assumes only the availability of similari ty (dissimilarity) information, and the number of parameters in the transformation does not depend on the number of features . Besides feature weighting, it can also be regarded as perfor ming nonparametric kernel adaptation. Experimental results on both toy and real-world datasets show promising results.