Visualizing the embedding of objects in Euclidean space

Michael Littman, Deborah F. Swayne, Nathaniel Dean, and Andreas Buja

Bellcore

ABSTRACT

Matrices representing dissimilarities within a set of objects are
familiar in mathematics, statistics and psychology.  In this paper we
describe XGvis, a software system which accepts diverse input data,
such as graphs and multivariate data, develops a dissimilarity matrix
from the data, and then iteratively and interactively embeds objects
in a Euclidean space of arbitrary dimension.  Using a technique called
multidimensional scaling, objects are positioned so that their
pairwise distances match the target dissimilarities as well as
possible.  Users can interact with XGobi, a software system for
visualizing high-dimensional data, to browse the resulting embeddings.
Mathematicians and statisticians have found XGvis to be useful for
discovering and exploring structure.  XGvis runs under the X Window
System(TM).