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Geometric Dirichlet Means Algorithm for topic inference
Mikhail Yurochkin · XuanLong Nguyen

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #128 #None

We propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end we study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Our method involves a fast optimization based weighted clustering procedure augmented with geometric corrections, which overcomes the computational and statistical inefficiencies encountered by other techniques based on Gibbs sampling and variational inference, while achieving the accuracy comparable to that of a Gibbs sampler. The topic estimates produced by our method are shown to be statistically consistent under some conditions. The algorithm is evaluated with extensive experiments on simulated and real data.

Author Information

Mikhail Yurochkin (University of Michigan)

I am working as a Research Staff Member at the IBM Research AI in Cambridge. Before, I have completed PhD in Statistics at the University of Michigan, advised by Prof. Long Nguyen. I received my bachelor degree in applied mathematics and physics from Moscow Institute of Physics and Technology.

XuanLong Nguyen (University of Michigan)

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