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Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments Vellido Alcacena, Alfredo Universitat Politècnica de Catalunya. Departament de Ciències de la Computació Universitat Politècnica de Catalunya. SOCO - Soft Computing Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Generative topographic mapping GTM Gaussian mixture models Outliers Student t-distributions 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. Postprint (published version) 2004-09 External research report Vellido, A. "Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments". 2004. https://hdl.handle.net/2117/97911 eng LSI-04-44 Open Access 12 p. application/postscript