Learning from LDA using Deep Neural Networks

Dongxu Zhang; Tianyi Luo; Dong Wang; Rong Liu

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arXiv · 1508.01011

Learning from LDA using Deep Neural Networks

Dongxu Zhang·Tianyi Luo·Dong Wang·Rong Liu

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Abstract

Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference. In spite of its great success, inferring the latent topic distribution with LDA is time-consuming. Motivated by the transfer learning approach proposed by~\newcite{hinton2015distilling}, we present a novel method that uses LDA to supervise the training of a deep neural network (DNN), so that the DNN can approximate the costly LDA inference with less computation. Our experiments on a document classification task show that a simple DNN can learn the LDA behavior pretty well, while the inference is speeded up tens or hundreds of times.

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BibTeX RIS

Dongxu Zhang, Tianyi Luo, Dong Wang, Rong Liu. 2015-08-05. Learning from LDA using Deep Neural Networks. https://arxiv.org/abs/1508.01011

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