@techreport{4c26c40907dd40199cc59e803080e22c,
title = "GTM-based Data Visualisation with Incomplete Data",
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.",
keywords = "Generative Topographic Mapping (GTM), missing values, Expectation -Maximisation (EM), hierarchical, visualisation plots",
author = "Yi Sun and Peter Tino and Nabney, {Ian T.}",
note = "Copyright {\textcopyright} 2001, Yi Sun, Peter Tino and Ian Nabney. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).",
year = "2001",
language = "English",
series = "NCRG",
publisher = "Aston University",
number = "2001/013",
type = "WorkingPaper",
institution = "Aston University",
}