Domain Transfer Dimensionality Reduction via Discriminant Kernel Learning

Abstract

Kernel discriminant analysis (KDA) is a popular technique for discriminative dimensionality reduction in data analysis. But, when a limited number of labeled data is available, it is often hard to extract the required low dimensional representation from a high dimensional feature space. Thus, one expects to improve the performance with the labeled data in other domains. In this paper, we propose a method, referred to as the domain transfer discriminant kernel learning (DTDKL), to find the optimal kernel by using the other labeled data from out-of-domain distribution to carry out discriminant dimensionality reduction. Our method learns a kernel function and discriminative projection by maximizing the Fisher discriminant distance and minimizing the mismatch between the in-domain and out-of-domain distributions simultaneously, by which we may get a better feature space for discriminative dimensionality reduction with cross-domain.