Computer Science – Learning
Scientific paper
2011-10-21
Computer Science
Learning
Scientific paper
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.
Graepel Thore
Hennig Philipp
Herbrich Ralf
Stern David
No associations
LandOfFree
Kernel Topic Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Kernel Topic Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Kernel Topic Models will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-84911