Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction

Abstract

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by the kernel trick.