Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Category learning through multimodality sensing
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
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
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Kernel Learning for Local Learning Based Clustering
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Convex Mixture Models for Multi-view Clustering
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Linear time maximum margin clustering
IEEE Transactions on Neural Networks
Estimation of mixture models using Co-EM
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis.