Topicmodeling for large-scale text data

2015年第16卷第6期

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《信息与电子工程前沿（英文）》 >> 2015年第16卷第6期 doi: 10.1631/FITEE.1400352

Topicmodeling for large-scale text data

1. College of Computer Science and Technology, Jilin University, Changchun 130012, China.2. MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun 130012, China

发布日期： 2016-01-05

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摘要

This paper develops a novel online algorithm, namely moving average stochastic variational inference (MASVI), which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ‘stochastic variational inference’ and ‘SGRLD’, our algorithm achieves a faster convergence rate and better performance.

关键词

Latent Dirichlet allocation (LDA) ; Topic modeling ; Online learning ; Moving average