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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 12/2018

26-04-2017 | Original Article

Self-organizing weighted incremental probabilistic latent semantic analysis

Authors: Ning Li, Wenjuan Luo, Kun Yang, Fuzhen Zhuang, Qing He, Zhongzhi Shi

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2018

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Abstract

PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.
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Footnotes
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Metadata
Title
Self-organizing weighted incremental probabilistic latent semantic analysis
Authors
Ning Li
Wenjuan Luo
Kun Yang
Fuzhen Zhuang
Qing He
Zhongzhi Shi
Publication date
26-04-2017
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0681-9

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