Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Information theory and statistics: a tutorial
Communications and Information Theory
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
Estimation of mixture models using Co-EM
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Data with multiple representations (views) arise naturally in many applications and multi-view algorithms can substantially improve the classification and clustering results. In this work, we study the problem of multi-view clustering and propose a multi-view convex mixture model that locates exemplars (cluster representatives) in the dataset by simultaneously considering all views. Convex mixture models are simplified mixture models that exhibit several attractive characteristics. The proposed algorithm extends the single view convex mixture models so as to handle data with any number of representations, taking into account the diversity of the views while preserving their good properties. Empirical evaluations on synthetic and real data demonstrate the effectiveness and potential of our method.