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Topic modeling for large-scale text data

Article

Abstract

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.

Key words

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

CLC number

TP391.1 

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Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.MOE Key Laboratory of Symbolic Computation and Knowledge EngineeringJilin UniversityChangchunChina

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