Scalable Inference for Latent Dirichlet Allocation

James Petterson; Tiberio Caetano

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arXiv · 0909.4603

Scalable Inference for Latent Dirichlet Allocation

James Petterson·Tiberio Caetano

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Abstract

We investigate the problem of learning a topic model - the well-known Latent Dirichlet Allocation - in a distributed manner, using a cluster of C processors and dividing the corpus to be learned equally among them. We propose a simple approximated method that can be tuned, trading speed for accuracy according to the task at hand. Our approach is asynchronous, and therefore suitable for clusters of heterogenous machines.

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James Petterson, Tiberio Caetano. 2009-09-25. Scalable Inference for Latent Dirichlet Allocation. https://arxiv.org/abs/0909.4603

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