@article{355ed1f3cbd4433f972357c3179116f5,
title = "GTM: the generative topographic mapping",
abstract = "Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.",
keywords = "Latent variable models, probability density, variables, linear transformations, latent space, data space, non-linear, generative topographic mapping, EM algorithm, elf-Organizing Map",
author = "Bishop, {Christopher M.} and Markus Svens{\'e}n and Williams, {Christopher K. I.}",
year = "1998",
month = jan,
day = "1",
language = "English",
volume = "10",
pages = "215--235",
journal = "Neural Computation",
issn = "0899-7667",
publisher = "MIT Press Journals",
number = "1",
}