Topic detection in cross-media: a semi-supervised co-clustering approach

With the rapid development of social media, the topics emerge and propagate in a variety of media websites. Although much work has been done since NIST proposed the problem of topic detection and tracking (TDT), most of them focus on single media data and are mainly based on unsupervised clustering method, which does not use some side information to help detecting topics. Therefore, traditional TDT approaches are not competent for cross-media topic detection. To efficiently use the information contained in multi-modal data from different sources and the prior knowledge, we propose a semi-supervised co-clustering approach for cross-media topic detection by a constrained non-negative matrix factorization. The correctness and convergence of our approach are proved to demonstrate its mathematical rigorousness. Experiments on the cross-media dataset verify the effectiveness of our proposed approach.