GTM-based data visualisation with incomplete data

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Abstract

We analyse how the Generative Topographic Mapping (GTM) can be modified to cope with missing values in the training data. Our approach is based on an Expectation -Maximisation (EM) method which estimates the parameters of the mixture components and at the same time deals with the missing values. We incorporate this algorithm into a hierarchical GTM. We verify the method on a toy data set (using a single GTM) and a realistic data set (using a hierarchical GTM). The results show our algorithm can help to construct informative visualisation plots, even when some of the training points are corrupted with missing values.

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Original languageEnglish
Place of PublicationBirmingham, UK
PublisherAston University
Number of pages9
ISBN (Print)NCRG/2001/013
StateUnpublished - 2001

    Keywords

  • Generative Topographic Mapping (GTM), missing values, Expectation -Maximisation (EM), hierarchical, visualisation plots

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