A generative model for sparse discrete binary data with non-uniform categorical priors.
Mark Girolami · The European Symposium on Artificial Neural Networks · 2000
The Generative T opographicMapping (GTM)was developed and introduced as a principle dalternativ eto the Self-Organising Map for, principally, visualising high dimensional continuous data. There are many cases where the observation data is ordinal and discrete and the application of methods developed speci cally for continuous data is inappropriate. Based on the continuous GTM data model a non-linear latent variable model for modeling sparse high dimensional binary data is presen ted. The primary motivation forthis w ork is the requirement for a dense and low dimensional representation of sparse binary vector space models of text documents based on the multiv ariate Bernoulli event model. The method is however applicable to binary data in general.