Geodesic Discriminant Analysis on Curved Riemannian Manifold

Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional ...

In this paper, we present a geodesic discriminant analysis (GDA) algorithm, which generalize linear discriminant analysis (LDA) in linear manifold space to curved Riemannian manifold space, with the aid of Riemannian logarithmic map. Compared with LDA, ...

Classical linear discriminant analysis (LDA) has been applied to machine learning and pattern recognition successfully, and many variants based on LDA are proposed. However, the traditional LDA has several disadvantages as follows: Firstly, since ...