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Multimedia Tools and Applications

, Volume 78, Issue 1, pp 141–160 | Cite as

Multi-modal max-margin supervised topic model for social event analysis

  • Feng XueEmail author
  • Jianwei Wang
  • Shengsheng Qian
  • Tianzhu Zhang
  • Xueliang Liu
  • Changsheng Xu
Article
  • 109 Downloads

Abstract

In this paper, we proposed a novel multi-modal max-margin supervised topic model (MMSTM) for social event analysis by jointly learning the representation together with the classifier in a unified framework. Compared with existing methods, the proposed MMSTM model has several advantages. (1) The proposed model can utilize the classifier as the regularization term of our model to jointly learn the parameters in the generative model and max-margin classifier, and use the Gibbs sampling to learn parameters of the representation model and max-margin classifier by minimizing the expected loss function. (2) The proposed model is able to not only effectively mine the multi-modal property by jointly learning the latent topic relevance among multiple modalities for social event representation, but also exploit the supervised information by considering a discriminative max-margin classifier for event classification to boost the classification performance. (3) In order to validate the effectiveness of the proposed model, we collect a large-scale real-world dataset for social event analysis, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMSTM.

Keywords

Social event classification Multi-modal Max-margin Social media Topic model 

Notes

Acknowledgments

The work is supported by the National Key Research and Development Program of China (No. 2017YFB080 3301). This work is also supported by the National Natural Science Foundation of China (No.61772170, 614 72115, 61572498, 61532009, 61472379, 61572296).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Feng Xue
    • 1
    Email author
  • Jianwei Wang
    • 1
  • Shengsheng Qian
    • 2
  • Tianzhu Zhang
    • 2
  • Xueliang Liu
    • 1
  • Changsheng Xu
    • 1
    • 2
  1. 1.Hefei University of TechnologyHefeiChina
  2. 2.National Lab of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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