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Variational Bayesian generative topographic mapping

Autor
Olier, I.; Vellido, A.
Tipus d'activitat
Article en revista
Revista
Journal of mathematical modeling and algorithms
Data de publicació
2008-12
Volum
7
Número
4
Pàgina inicial
371
Pàgina final
387
DOI
https://doi.org/10.1007/s10852-008-9088-7 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/13279 Obrir en finestra nova
Resum
General finite mixture models are powerful tools for the density-based grouping of multivariate i.i.d. data, but they lack data visualization capabilities, which reduces their practical applicability to real-world problems. Generative topographic mapping (GTM) was originally formulated as a constrained mixture of distributions in order to provide simultaneous visualization and clustering of multivariate data. In its inception, the adaptive parameters were determined by maximum likelihood (ML), u...
Citació
Olier, I.; Vellido, A. Variational Bayesian generative topographic mapping. "Journal of mathematical modeling and algorithms", Desembre 2008, vol. 7, núm. 4, p. 371-387.
Paraules clau
Adaptive regularization, Clustering, Data visualization, Generative topographic mapping, Overfitting, Variational methods
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants