Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

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Abstract

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

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Original languageEnglish
Place of PublicationBirmingham
PublisherAston University
Number of pages29
ISBN (Print)NCRG/2008/006
StatePublished - 25 Sep 2008

    Keywords

  • Including prior information of the covariance structure, Generative Topographic Mapping, Improving Data Visualisation

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