Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Matrix Factorization Approach for Integrating Multiple Data Views
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Multi-view clustering of multilingual documents
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Multi-view data is common in a wide variety of application domains. Properly exploiting the relations among different views is helpful to alleviate the difficulty of a learning problem of interest. To this end, we propose an extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA). CoPLSA integrates individual PLSAs in different views by pairwise co-regularization. The central idea behind the co-regularization is that the sample similarities in the topic space from one view should agree with those from another view. An EM-based scheme is employed for parameter estimation, and a local optimal solution is obtained through an iterative process. Extensive experiments are conducted on three real-world datasets and the compared results demonstrate the superiority of our approach.