- Published 1998 in Neural Computation

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 transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm. GTM provides a principled alternative to the widely used self-organizing map (SOM) of Kohonen (1982) and overcomes most of the signicant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from ow diagnostics for a multiphase oil pipeline.

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@article{Bishop1998GTMTG,
title={GTM: The Generative Topographic Mapping},
author={Christopher M. Bishop and Markus Svens{\'e}n and Christopher K. I. Williams},
journal={Neural Computation},
year={1998},
volume={10},
pages={215-234}
}