Hierarchical models for data visualization

M.E. Tipping, C.M. Bishop

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

Abstract

Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of data. Most visualization 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-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximisation algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines
Original languageEnglish
Title of host publicationArtificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
PublisherIET
Pages70-75
Number of pages6
ISBN (Print)0-85296-690-3
DOIs
Publication statusPublished - 1 Jul 1997

Keywords

  • data visualisation
  • data interpretation
  • data space
  • data visualization
  • expectation-maximisation algorithm
  • hierarchical mixture
  • hierarchical models
  • high-dimensional space
  • latent variable models
  • multi-phase flows

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