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)


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.


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



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.


  1. 1.
    Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Blei, D., Lafferty, J.: A correlated topic model of science. Ann. Appl. Sci. 1, 17–35 (2007)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Blei, D., David, M., Ng, A., Jordan, M., Lafferty, J.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Putthividhy, D., Attias, H.T., Magarajan, S.S.: Topic regression multi-modal latent Dirichlet allocation for image annotation. In: Computer Vision and Pattern Recognition, vol. 1, pp. 3408–3415 (2010)Google Scholar
  5. 5.
    Allan, J., Carbonell, J., Doddington, G., Yamron, J., et al.: Topic detection and tracking pilot study final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, pp. 194–218 (1998)Google Scholar
  6. 6.
    Cao, J., Ngo, C., Zhang, Y., Li, J.: Tracking web video topics: discovery, visualization, and monitoring. IEEE Trans. Circuits Syst. Video Technol. 21(12), 1835–1846 (2011)CrossRefGoogle Scholar
  7. 7.
    Chen, J., Li, K., Zhu, J., Chen, W.: WarpLDA: a cache efficient o(1) algorithm for latent Dirichlet allocation. Proc. VLDB Endow. 9(10), 744–755 (2015)CrossRefGoogle Scholar
  8. 8.
    Mairal, J.: Optimization with first-order surrogate functions. In: ICML (2013)Google Scholar
  9. 9.
    Mairal, J.: Stochastic majorization-minimization algorithms for large-scale optimization. In: International Conference on Neural Information Processing Systems, vol. 2, pp. 2283–2291 (2013)Google Scholar
  10. 10.
    Pang, J., Jia, F., Zhang, C., Zhang, W., Huang, Q., Yin, B.: Unsupervised web topic detection using a ranked clustering-like pattern across similarity cascades. IEEE Trans. Multimed. 17(6), 843–853 (2015)CrossRefGoogle Scholar
  11. 11.
    Pang, J., Tao, F., Zhang, C., Zhang, W., Huang, Q., Yin, B.: Robust latent poisson deconvolution from multiple features for web topic detection. IEEE Trans. Multimed. 18(12), 2482–2493 (2016)CrossRefGoogle Scholar
  12. 12.
    Pang, J., Tao, F., Li, L., Huang, Q., Yin, B., Tian, Q.: A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities. Neurocomputing 275, 478–487 (2018)CrossRefGoogle Scholar
  13. 13.
    Lange, K., Hunter, D.R., Yang, I.: Optimization transfer using surrogate objective functions. J. Comput. Graph. Stat. 9(1), 1–20 (2000)MathSciNetGoogle Scholar
  14. 14.
    Hannah, L.A.: Stochastic optimization. Int. Encycl. Soc. Behav. Sci. 5(5), 473–481 (2015)Google Scholar
  15. 15.
    Bottou, L., Bousquet, O.: The tradeoffs of large scale learning. In: International Conference on Neural Information Processing Systems, pp. 161–168 (2007)Google Scholar
  16. 16.
    Aiello, L.M., et al.: Sensing trending topics in Twitter. IEEE Trans. Multimed. 15(6), 1268–1282 (2013)CrossRefGoogle Scholar
  17. 17.
    Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Found. Trends\(\textregistered \) Mach. Learn. 1(1-2), 1–305 (2008)CrossRefGoogle Scholar
  18. 18.
    Roux, N.L., Schmidt, M., Bach, F.: A stochastic gradient method with an exponential convergence rate for finite training sets. In: International Conference on Neural Information Processing Systems, vol. 2, pp. 2663–2671 (2012)Google Scholar
  19. 19.
    Cappé, O., Moulines, E.: On-line expectation-maximization algorithm for latent data models. J. R. Stat. Soc. 71(3), 593–613 (2009)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. In: International Conference on Neural Information Processing Systems, vol. 1, pp. 315–323 (2013)Google Scholar
  21. 21.
    Neal, R.M., Hinton, G.E.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan, M.I. (ed.) Learning in Graphical Models. ASID, vol. 89, pp. 355–368. Springer, Dordrecht (1998). Scholar
  22. 22.
    Papadopoulous, S., Zigkolis, C., Kompatsiaris, Y., Vakali, A.: Cluster-based landmark and event detection on tagged photo collections. IEEE Multimed. 18(1), 52–63 (2011)CrossRefGoogle Scholar
  23. 23.
    Debatty, T., Michiardi, P., Mees, W.: Fast online K-NN graph building. CoRR (2016)Google Scholar
  24. 24.
    Wu, X., Hauptmann, G., Ngo, C.: Novelty detection for cross-lingual news story with visual duplicates and speech transcripts. In: ACM Multimedia, pp. 168–177 (2007)Google Scholar
  25. 25.
    Wang, Y., Bai, H., Stanton, M., Chen, W.-Y., Chang, E.Y.: PLDA: parallel latent Dirichlet allocation for large-scale applications. In: Goldberg, A.V., Zhou, Y. (eds.) AAIM 2009. LNCS, vol. 5564, pp. 301–314. Springer, Heidelberg (2009). Scholar
  26. 26.
    Zhang, Y., Li, G., Chu, L., Wang, S., Zhang, W., Huang, Q.: Cross-media topic detection: a multi-modality fusion framework. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)Google Scholar
  27. 27.
    Liu, Z., Zhang, Y., Chang, E.Y., Sun, M.: PLDA+: parallel latent Dirichlet allocation with data placement and pipeline processing. ACM Trans. Intell. Syst. Technol. 2(3), 26:1–26:18 (2011)CrossRefGoogle Scholar

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

Personalised recommendations