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.
https://hal.inria.fr/hal-01916220
Contributor : Rodolphe Priam <>
Submitted on : Thursday, November 8, 2018 - 12:43:35 PM Last modification on : Saturday, June 8, 2019 - 2:20:04 PM
Identifiers
HAL Id : hal-01916220, version 1
Citation
Rodolphe Priam, Mohamed Nadif. Generative topographic mapping and factor analyzers. Proceeding of the 1st International Conference on Pattern Recognition Applications and Methods, Feb 2012, Vilamoura, Portugal. pp.284-287. ⟨hal-01916220⟩