GENERATIVE TOPOGRAPHIC MAPPING AND FACTOR ANALYZERS

Rodolphe Priam, Mohamed Nadif

Abstract

By embedding random factors in the Gaussian mixture model (GMM), we propose a new model called faGTM. Our approach is based on a flexible hierarchical prior for a generalization of the generative topographic mapping (GTM) and the mixture of principal components analyzers (MPPCA). The parameters are estimated with expectation-maximization and maximum a posteriori. Empirical experiments show the interest of our proposal.

References

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Paper Citation


in Harvard Style

Priam R. and Nadif M. (2012). GENERATIVE TOPOGRAPHIC MAPPING AND FACTOR ANALYZERS . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 284-287. DOI: 10.5220/0003765202840287


in Bibtex Style

@conference{icpram12,
author={Rodolphe Priam and Mohamed Nadif},
title={GENERATIVE TOPOGRAPHIC MAPPING AND FACTOR ANALYZERS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={284-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003765202840287},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - GENERATIVE TOPOGRAPHIC MAPPING AND FACTOR ANALYZERS
SN - 978-989-8425-98-0
AU - Priam R.
AU - Nadif M.
PY - 2012
SP - 284
EP - 287
DO - 10.5220/0003765202840287