Robust kernel discriminant analysis and its application to feature extraction and recognition

Abstract

Subspace analysis is an effective technique for dimensionality reduction, which aims at finding a low-dimensional space of high-dimensional data. In this paper, a novel subspace method called robust kernel discriminant analysis is proposed for dimensionality reduction. An optimization function is firstly defined in terms of the distance between similar elements and the distance between dissimilar elements, which can preserve the structure of the data in the mapping space. Then the optimization function is transformed into an eigenvalue problem and the projection vectors are obtained by solving the eigenvalue problem. Finally, experimental results on face images and handwritten numerical characters demonstrate the effectiveness and feasibility of the proposed method.