Paper Info
Title
Multi-modal max-margin supervised topic model for social event analysis.
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.
Year
DOI
Venue
2019
10.1007/s11042-017-5605-x
Multimedia Tools Appl.
Keywords
Field
DocType
Social event classification, Multi-modal, Max-margin, Social media, Topic model
Pattern recognition,Computer science,Exploit,Regularization (mathematics),Artificial intelligence,Topic model,Classifier (linguistics),Discriminative model,Machine learning,Gibbs sampling,Modal,Generative model
Journal
Volume
Issue
ISSN
78
1
1573-7721
Citations 
PageRank 
References 
1
0.36
29
Authors
6
Name
Order
Citations
PageRank
Feng Xue1156.03
Jianwei Wang26112.57
Shengsheng Qian313019.10
Tianzhu Zhang4170582.80
Xueliang Liu57615.56
Changsheng Xu64957332.87