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

  • Jinzhong Lin
  • Junbiao Pang
  • Li Su
  • Yugui Liu
  • Qingming HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

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.

Keywords

Large-scale Poisson Deconvolution Unsupervised ranking Web topic detection Surrogate function 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jinzhong Lin
    • 1
  • Junbiao Pang
    • 2
  • Li Su
    • 1
  • Yugui Liu
    • 1
  • Qingming Huang
    • 1
    • 3
    Email author
  1. 1.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  3. 3.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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