Modeling text with generalizable Gaussian mixtures
Lars Kai Hansen, Snævar Sigurðsson, Thomas Kolenda, Finn Årup Nielsen, Ulrik Kjems, Jan Otto Larsen · 2002
We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.