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Self-organizing weighted incremental probabilistic latent semantic analysis

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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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  1. http://news.163.com/special/.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 91546122, 61602438, 61573335, 61473273, 61473274, 61363058), National High-tech R&D Program of China (863 Program) (No. 2014AA015105), National Science and Technology Support Program (No. 2014BAK02B07), National major R&D program of Beijing Municipal Science & Technology Commission (Z161100002616032), Guangdong provincial science and technology plan projects (No. 2015 B 010109005).

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Ning Li

  2. The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Ning Li, Wenjuan Luo, Fuzhen Zhuang, Qing He & Zhongzhi Shi

  3. National Institute of Metrology, Beijing, 100029, China

    Kun Yang

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  1. Ning Li
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  2. Wenjuan Luo
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  3. Kun Yang
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  5. Qing He
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  6. Zhongzhi Shi
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Correspondence to Ning Li.

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Li, N., Luo, W., Yang, K. et al. Self-organizing weighted incremental probabilistic latent semantic analysis. Int. J. Mach. Learn. & Cyber. 9, 1987–1998 (2018). https://doi.org/10.1007/s13042-017-0681-9

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