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Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11295))

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Abstract

Organizing webpages into hot topics is one of the key steps to understand the trends from multi-modal web data. To handle this pressing problem, Poisson Deconvolution (PD), a state-of-the-art method, recently is proposed to rank the interestingness of web topics on a similarity graph. Nevertheless, in terms of scalability, PD optimized by expectation-maximization is not sufficiently efficient for a large-scale data set. In this paper, we develop a Stochastic Poisson Deconvolution (SPD) to deal with the large-scale web data sets. Experiments demonstrate the efficacy of the proposed approach in comparison with the state-of-the-art methods on two public data sets and one large-scale synthetic data set.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China: 61332016, 61472389, 61672069, 61872333, 61650202 and U1636214, in part by Key Research Program of Frontier Sciences, CAS: QYZDJ-SSW-SYS013.

Author information

Authors and Affiliations

  1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China

    Jinzhong Lin, Li Su, Yugui Liu & Qingming Huang

  2. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Junbiao Pang

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Qingming Huang

Authors
  1. Jinzhong Lin
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  2. Junbiao Pang
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  3. Li Su
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  4. Yugui Liu
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  5. Qingming Huang
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Corresponding author

Correspondence to Qingming Huang .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

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Lin, J., Pang, J., Su, L., Liu, Y., Huang, Q. (2019). Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_49

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  • DOI: https://doi.org/10.1007/978-3-030-05710-7_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05709-1

  • Online ISBN: 978-3-030-05710-7

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