Missing data imputation through GTM as a mixture of t-distributions

The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural networkinspired, Self-Organizing Maps. The GTM can also be interpreted as a constrained mixture of distribution models. 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 paper, the GTM is redefined as a constrained mixture of t-distributions: the t-GTM, and the Expectation

• Type: Journal
• Year: 2006
• Where: NN
• Authors: Alfredo Vellido
• Keywords: Constrained Mixture, Generative Topographic Mapping, Neural Networks, NN 2006, Student T-distributions