Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
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-d...
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2004 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/97911 |
| Acceso en línea: | https://hdl.handle.net/2117/97911 |
| Access Level: | acceso abierto |
| Palabra clave: | Generative topographic mapping GTM Gaussian mixture models Outliers Student t-distributions Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | 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. |
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