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Variational GTM

Autor
Vellido, A.; Olier, I.
Tipus d'activitat
Article en revista
Revista
Lecture notes in computer science
Data de publicació
2007-12
Volum
4881
Pàgina inicial
77
Pàgina final
86
DOI
https://doi.org/10.1007/978-3-540-77226-2_9 Obrir en finestra nova
URL
https://link.springer.com/chapter/10.1007/978-3-540-77226-2_9 Obrir en finestra nova
Resum
Generative Topographic Mapping (GTM) is a non-linear latent variable model that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as a constrained mixture of distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this paper, we define an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Process (GP)-base...
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants