GTM-based data visualisation with incomplete data

Yi Sun, Peter Tino, Ian T. Nabney

Research output: Other contribution

11 Downloads (Pure)


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.
Original languageEnglish
PublisherAston University
Publication statusPublished - 2001


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


Dive into the research topics of 'GTM-based data visualisation with incomplete data'. Together they form a unique fingerprint.

Cite this