Applications of Discriminative Dimensionality Reduction

Abstract

Discriminative nonlinear dimensionality reduction aims at a visualization of a given set of data such that the information contained in the data points which is of particular relevance for a given class labeling is displayed. We link this task to an integration of the Fisher information, and we discuss its difference from supervised classification. We present two potential application areas: speed-up of unsupervised nonlinear visualization by integration of prior knowledge, and visualization of a given classifier such as an SVM in low dimensions.