A hierarchical latent variable model for data visualization


Visualization has proven to be a powerful and widely-applicable tool 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-maximization 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 and to data in 36 dimensions derived from satellite images.

Publication DOI: https://doi.org/10.1109/34.667885
Divisions: Aston University (General)
Additional Information: Copyright of Institute of Electrical and Electronics Engineers (IEEE)
Uncontrolled Keywords: Latent variables,data visualization,EM algorithm,hierarchical mixture model,density estimation,principal component analysis,factor analysis,maximum likelihood,clustering,statistics.
Publication ISSN: 1939-3539
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Related URLs: https://ieeexpl ... document/667885 (Publisher URL)
PURE Output Type: Article
Published Date: 1998-03
Authors: Bishop, Christopher M.
Tipping, Michael E.



Version: Published Version

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