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.
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⟩