GTM-based data visualisation with incomplete data


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.

Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Uncontrolled Keywords: Generative Topographic Mapping (GTM),missing values,Expectation -Maximisation (EM),hierarchical,visualisation plots
ISBN: NCRG/2001/013
PURE Output Type: Technical report
Published Date: 2001
Authors: Sun, Yi
Tino, Peter
Nabney, Ian T. ( 0000-0003-1513-993X)


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