Visualizing data in high-dimensional spaces

Abstract

A novel approach to the analysis of feature spaces in statistical pattern recognition is described. This approach starts with linear dimensionality reduction, followed by the computation of selected sections through and projections of feature space. A number of representative feature spaces are analysed in this way; we find linear reduction to be surprisingly successful, and in the real-world data sets we have examined, typical classes of objects are only moderately complicated.