A principled approach to interactive hierarchical non-linear visualization of high-dimensional data

P. Tino, I. Nabney, Yi. Sun, B.S. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hierarchical visualization systems are desirable because a single twodimensional
visualization plot may not be sufficient to capture all of the
interesting aspects of complex high-dimensional data sets. We extend
an existing locally linear hierarchical visualization system PhiVis [1] in
several directions: (1) we allow for non-linear projection manifolds (the
basic building block is the Generative Topographic Mapping – GTM), (2)
we introduce a general formulation of hierarchical probabilistic models
consisting of local probabilistic models organized in a hierarchical tree,
(3) we describe folding patterns of low-dimensional projection manifold
in high-dimensional data space by computing and visualizing the manifold’s
local directional curvatures. Quantities such as magnification factors
[3] and directional curvatures are helpful for understanding the layout
of the nonlinear projection manifold in the data space and for further
refinement of the hierarchical visualization plot. Like PhiVis, our system
is statistically principled and is built interactively in a top-down fashion
using the EM algorithm. We demonstrate the visualization system principle
of the approach on a complex 12-dimensional data set and mention
possible applications in the pharmaceutical industry.
Original languageEnglish
Title of host publicationInterface '01 - Frontiers in Data Mining and Bioinformatics
Publication statusPublished - 2001


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