A HIERARCHICAL LATENT VARIABLE MODEL FOR DATA VISUALIZATION

Citation
Cm. Bishop et Me. Tipping, A HIERARCHICAL LATENT VARIABLE MODEL FOR DATA VISUALIZATION, IEEE transactions on pattern analysis and machine intelligence, 20(3), 1998, pp. 281-293
Citations number
18
Categorie Soggetti
Computer Science Artificial Intelligence","Computer Science Artificial Intelligence","Engineering, Eletrical & Electronic
ISSN journal
01628828
Volume
20
Issue
3
Year of publication
1998
Pages
281 - 293
Database
ISI
SICI code
0162-8828(1998)20:3<281:AHLVMF>2.0.ZU;2-Z
Abstract
Visualization has proven to be a powerful and widely-applicable tool f or the analysis and interpretation of multivariate data. Most visualiz ation algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space, it is unlikely that a single two-d imensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allow s the complete data set to be visualized at the top level, with cluste rs and subclusters of data points visualized at deeper levels. The alg orithm is based on a hierarchical mixture of latent variable models, w hose parameters are estimated using the expectation-maximization algor ithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic da ta set in 12 dimensions obtained from a simulation of multiphase flows in oil pipelines, and to data in 36 dimensions derived from satellite images. A Matlab software implementation of the algorithm is publicly available from the World Wide Web.