Missing data imputation through generative topographic mapping as a mixture of t-distributions: Theoretical developments
The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural network-inspired, Self-Organizing Map (SOM). The GTM can also be interpreted as a constrained mixture of distributions model. In recent years, much attention has been directed t...
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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:2117/83308 |
| Acceso en línea: | https://hdl.handle.net/2117/83308 |
| Access Level: | acceso abierto |
| Palabra clave: | Missing data Outliers Generative topographic mapping Student multivariate t-distributions Robust imputation Data visualization Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural network-inspired, Self-Organizing Map (SOM). The GTM can also be interpreted as a constrained mixture of distributions model. In recent years, much attention has been directed towards Student t-distributions as an alternative to Gaussians in mixture models due to their robustness towards outliers. In this report, the GTM is redefined as a constrained mixture of t-distributions: the t-GTM, and the Expectation-Maximization algorithm that is used to fit the model to the data is modified to provide missing data imputation. |
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