GTM: A Principled Alternative to the Self-Organizing Map

Christopher M. Bishop, Markus Svensen, Christopher K. I. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (for Generative Topographic Map), which allows general non-linear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance of the GTM algorithm on simulated data from flow diagnostics for a multi-phase oil pipeline.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 9
PublisherMIT Press
Number of pages7
Publication statusPublished - 1997


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