Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multiview clustering: a late fusion approach using latent models
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multi-view learning via probabilistic latent semantic analysis
Information Sciences: an International Journal
Improving document clustering using automated machine translation
Proceedings of the 21st ACM international conference on Information and knowledge management
Co-regularized PLSA for multi-view clustering
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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We propose a new multi-view clustering method which uses clustering results obtained on each view as a voting pattern in order to construct a new set of multi-view clusters. Our experiments on a multilingual corpus of documents show that performance increases significantly over simple concatenation and another multi-view clustering technique.