Markov random field modeling in computer vision
Markov random field modeling in computer vision
Unsupervised classification learning from cross-modal environment structure
Unsupervised classification learning from cross-modal environment structure
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multivariate Information Bottleneck
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Data weaving: scaling up the state-of-the-art in data clustering
Proceedings of the 17th ACM conference on Information and knowledge management
A graph model for mutual information based clustering
Journal of Intelligent Information Systems
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A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comrafs in perspective by showing their relationship with several existing models. Since it can be problematic to apply existing inference techniques for graphical models to Comrafs, we design two simple and efficient inference algorithms specific for Comrafs, which are based on combinatorial optimization. We show that even such simple algorithms consistently and significantly outperform Latent Dirichlet Allocation (LDA) on a document clustering task. We then present Comraf models for semi-supervised clustering and transfer learning that demonstrate superior results in comparison to an existing semi-supervised scheme (constrained optimization).