GTM: A Principled Alternative to the Self-Organizing mAP
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 heuris(cid:173) tic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probabil(cid:173) ity 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 algo(cid:173) rithm (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) algo(cid:173) rithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the per(cid:173) formance of the GTM algorithm on simulated data from flow diag(cid:173) nostics for a multi-phase oil pipeline.
Cite
Text
Bishop et al. "GTM: A Principled Alternative to the Self-Organizing mAP." Neural Information Processing Systems, 1996.Markdown
[Bishop et al. "GTM: A Principled Alternative to the Self-Organizing mAP." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/bishop1996neurips-gtm/)BibTeX
@inproceedings{bishop1996neurips-gtm,
title = {{GTM: A Principled Alternative to the Self-Organizing mAP}},
author = {Bishop, Christopher M. and Svensén, Markus and Williams, Christopher K. I.},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {354-360},
url = {https://mlanthology.org/neurips/1996/bishop1996neurips-gtm/}
}