Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The multiinformation function as a tool for measuring stachastic dependence
Learning in graphical models
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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
Multivariate Information Bottleneck
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
The minimum information principle for discriminative learning
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning decision trees with taxonomy of propositionalized attributes
Pattern Recognition
Type-based categorization of relational attributes
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Improving clustering stability with combinatorial MRFs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Identification over multiple databases
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Symmetry breaking in soft clustering decoding of neural codes
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Multi-information ensemble diversity
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
The multi-feature information bottleneck with application to unsupervised image categorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.