Recent work has established that digital images of a human face, collected under various
illumination conditions, contain discriminatory information that can be used in classification …

This paper presents several geometrically motivated techniques for the visualization of high-
dimensional biological data sets. The Grassmann manifold provides a robust framework for …

We extend the self-organizing mapping algorithm to the problem of visualizing data on
Grassmann manifolds. In this setting, a collection of k points in n-dimensions is represented …

This dissertation consists of four parts. It introduces a novel geometric framework for the
general classification problem and presents empirical results obtained from applying the …

We propose a method for extending Kohonen's self-organizing mapping to the geometric
framework of the Grassmannian. The resulting algorithm serves as a prototype of the …

The pursuit of low dimensional structure from high dimensional data leads in many
instances to the finding the lowest rank matrix among a parameterized family of matrices. In …

This dissertation develops numerical algorithms for comparing sets of data sets utilizing
shape and orientation of data clouds. Two key components for" comparing" are the distance …

Bruce A. Draper draper@cs.colostate.edu Professional Experience Education Journal
Articles (Academic) Page 1 Bruce A. Draper draper@cs.colostate.edu Professional …