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A Novel Multiview Topic Model to Compute Correlation of Heterogeneous Data

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  • Jinsheng Shen

    (Fudan University)

  • Mingmin Chi

    (Fudan University)

Abstract

With fast development of Internet technologies and sensor techniques, it is much easier to acquire data from different sources in different dates and times. However, how to compute the correlation of those heterogeneous data is a big challenge for data mining and information retrieval. Here, data feature from one source is called as a view, and the multiview features denote the same data point. In the paper, hidden correlation of two-view features is proposed to construct a Heterogeneous (multiview) Topic Model (HTM). In particular, probabilistic topic model is utilized for different views as usually, generative models provide much richer features when handling high-dimensional data such as texts. Nevertheless, it is necessary to know the form of probability distribution for most existent probabilistic topic models, such as latent Dirichlet allocation. By avoiding the limitation of probabilistic topic model, the HTM is reduced to solving a non-negative matrix tri-factorization problem with certain constraints such that the proposed approach can be used in terms of an arbitrary model.

Suggested Citation

  • Jinsheng Shen & Mingmin Chi, 2018. "A Novel Multiview Topic Model to Compute Correlation of Heterogeneous Data," Annals of Data Science, Springer, vol. 5(1), pages 9-19, March.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:1:d:10.1007_s40745-017-0135-y
    DOI: 10.1007/s40745-017-0135-y
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    Cited by:

    1. Chonghui Guo & Menglin Lu & Wei Wei, 2021. "An Improved LDA Topic Modeling Method Based on Partition for Medium and Long Texts," Annals of Data Science, Springer, vol. 8(2), pages 331-344, June.

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