CLC number: TP391.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-05-07
Cited: 3
Clicked: 7419
Xi-ming Li, Ji-hong Ouyang, You Lu. Topic modeling for large-scale text data[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 457-465.
@article{title="Topic modeling for large-scale text data",
author="Xi-ming Li, Ji-hong Ouyang, You Lu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="6",
pages="457-465",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400352"
}
%0 Journal Article
%T Topic modeling for large-scale text data
%A Xi-ming Li
%A Ji-hong Ouyang
%A You Lu
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 6
%P 457-465
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400352
TY - JOUR
T1 - Topic modeling for large-scale text data
A1 - Xi-ming Li
A1 - Ji-hong Ouyang
A1 - You Lu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 6
SP - 457
EP - 465
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1400352
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.
Overall, I liked the idea introduced by the paper, as well as the large empirical case study. Scaling up topic models without loss of precision indeed is an important area.
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