Hierarchical probabilistic principal component subspaces for data visualization

Yue Wang, Lan Luo, Matthew T. Freedman, Sun Yuan Kung

Research output: Contribution to conferencePaper

Abstract

Visual exploration has proven to be a powerful tool for multivariate data mining. Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space. To reveal all of the interesting aspects of complex data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The methods involve multiple use of standard finite normal mixture models and probabilistic principal component projections, whose parameters are estimated using the expectation-maximization and principal component neural networks under the information theoretic criteria. We demonstrate the principle of the approach on two three-dimensional synthetic data sets.

Original languageEnglish (US)
Pages2498-2503
Number of pages6
StatePublished - Dec 1 1999
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Wang, Y., Luo, L., Freedman, M. T., & Kung, S. Y. (1999). Hierarchical probabilistic principal component subspaces for data visualization. 2498-2503. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .