Methods for Matrix Factorization
June 2, 2017I gave a general overview talk at University of Sydney to the Business School. Funny how the statistics guys are often moving over to business schools!
Abstract: Matrix factorisation, and the related problems of topic models, co-factorisation, tensor factorisation, etc., when applied to discrete data use Boolean, Poisson. negative binomial and multinomial models of the entries. These are used in problems with text, document and graph analysis and bioinformatics, especially where data is big and sparse. We’re developing general techniques using Gibbs sampling with collapsed, auxiliary and latent variables as well as hierarchical modelling of priors. I’ll talk about the class of problems and give examples of some of the techniques. While simple, they scale well and admit multi-core implementation, often times producing state-of-the-art results.
The slides are here.
Bayesian Models 
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