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Nonparametric topic models such as hierarchical Dirichlet processes (HDP) have been attracting more and more attentions for multimedia data analysis. However, the existing models for multimedia data are unsupervised ones that purely cluster semantically or characteristically related features into a specific latent topic without considering side information such as class information. In this paper, we present a novel supervised sequential symmetric correspondence HDP (Sup-SSC-HDP) model fordoi:10.3169/mta.7.80 fatcat:fe6wbherr5a6tc5m4ww2nvnlyu