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


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.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Aston University (General)
Uncontrolled Keywords: Including prior information of the covariance structure,Generative Topographic Mapping,Improving Data Visualisation
ISBN: NCRG/2008/006
Last Modified: 27 Jun 2024 11:56
Date Deposited: 09 Sep 2009 12:30
PURE Output Type: Technical report
Published Date: 2008-09-25
Authors: Schroeder, Martin
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)
Cornford, Dan (ORCID Profile 0000-0001-8787-6758)


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