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Unfolding the Manifold in Generative Topographic Mapping

Autor
Cruz, R.; Vellido, A.
Tipus d'activitat
Article en revista
Revista
Lecture notes in computer science
Data de publicació
2008-09
Volum
5271
Pàgina inicial
392
Pàgina final
399
DOI
https://doi.org/10.1007/978-3-540-87656-4_49 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/9511 Obrir en finestra nova
URL
https://link.springer.com/chapter/10.1007%2F978-3-540-87656-4_49?LI=true Obrir en finestra nova
Resum
Generative Topographic Mapping (GTM) is a probabilistic latent variable model for multivariate data clustering and visualization. It tries to capture the relevant data structure by defining a low-dimensional manifold embedded in the high-dimensional data space. This requires the assumption that the data can be faithfully represented by a manifold of much lower dimension than that of the observed space. Even when this assumption holds, the approximation of the data may, for some datasets, require...
Citació
Cruz, R.; Vellido, A. Unfolding the Manifold in Generative Topographic Mapping. "Lecture notes in computer science", Setembre 2008, vol. 5271, p. 392-399.
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants