@inproceedings{bishop1997gtm,
author = {Bishop, Christopher and Svensén, Markus and Williams, Christopher K. I.},
title = {GTM: a principled alternative to the Self-Organizing Map},
booktitle = {International Conference on Artificial Neural Networks, ICANN'96},
year = {1997},
month = {January},
abstract = {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 Mapping), 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.},
publisher = {Springer},
url = {http://approjects.co.za/?big=en-us/research/publication/gtm-a-principled-alternative-to-the-self-organizing-map/},
pages = {165-170},
volume = {9},
edition = {International Conference on Artificial Neural Networks, ICANN'96},
}