dc.contributor
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor
Universitat Politècnica de Catalunya. SOCO - Soft Computing
dc.contributor.author
Vellido Alcacena, Alfredo
dc.identifier
Vellido, A. "Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments". 2004.
dc.identifier
https://hdl.handle.net/2117/97911
dc.description.abstract
The Generative Topographic Mapping (GTM: Bishop et al. 1998a), a non-linear latent variable model, was originally defined as constrained mixture of Gaussians. Gaussian mixture models are known to lack robustness in the presence of outlier observations in the data sample, and multivariate Student t-distributions have recently been put forward as a more robust alternative to deal with continuous data in this context.
dc.description.abstract
Postprint (published version)
dc.format
application/postscript
dc.subject
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject
Generative topographic mapping
dc.subject
Gaussian mixture models
dc.subject
Student t-distributions
dc.title
Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
dc.type
External research report
