Topic modeling for large-scale text data

被引：7

作者：

Li, Xi-ming ^{[1
,2
]}

Ouyang, Ji-hong ^{[1
,2
]}

Lu, You ^{[1
,2
]}

机构：

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China

[2] Jilin Univ, MOE Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China

来源：

FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING | 2015年 / 16卷 / 06期

基金：

中国国家自然科学基金;

关键词：

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

D O I：

10.1631/FITEE.1400352

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：457 / 465

页数：9

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