A hierarchical latent variable model for data visualization


Autoria(s): Bishop, Christopher M.; Tipping, Michael E.
Data(s)

01/03/1998

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.aston.ac.uk/675/1/IEEE_transactions_on_pattern_analysis_and_machine_intelligence_20(3)pdf.pdf

Bishop, Christopher M. and Tipping, Michael E. (1998). A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (3), pp. 281-293.

Relação

http://eprints.aston.ac.uk/675/

Tipo

Article

PeerReviewed