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Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #32

We propose a novel distribution that generalizes the Multinomial distribution to enable dependencies between dimensions. Our novel distribution is based on the parametric form of the Poisson MRF model [Yang et al., 2012] but is fundamentally different because of the domain restriction to a fixed-length vector like in a Multinomial where the number of trials is fixed or known. Thus, we propose the Fixed-Length Poisson MRF (LPMRF) distribution. We develop methods to estimate the likelihood and log partition function (i.e. the log normalizing constant), which was not possible with the Poisson MRF model. In addition, we propose novel mixture and topic models that use LPMRF as a base distribution and discuss the similarities and differences with previous topic models such as the recently proposed Admixture of Poisson MRFs [Inouye et al., 2014]. We show the effectiveness of our LPMRF distribution over Multinomial models by evaluating the test set perplexity on a dataset of abstracts and Wikipedia. Qualitatively, we show that the positive dependencies discovered by LPMRF are interesting and intuitive. Finally, we show that our algorithms are fast and have good scaling.

Author Information

David I Inouye (University of Texas at Austin)
Pradeep Ravikumar (University of Texas at Austin)
Inderjit Dhillon (University of Texas at Austin)

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