Proceedings of the 1998 conference on Advances in neural information processing systems II
Multivariate Information Bottleneck
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Using KCCA for Japanese---English cross-language information retrieval and document classification
Journal of Intelligent Information Systems
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Variational Bayesian mixture of robust CCA models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Unsupervised inference of auditory attention from biosensors
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Bayesian Canonical correlation analysis
The Journal of Machine Learning Research
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We introduce a mixture of probabilistic canonical correlation analyzers model for analyzing local correlations, or more generally mutual statistical dependencies, in cooccurring data pairs. The model extends the traditional canonical correlation analysis and its probabilistic interpretation in three main ways. First, a full Bayesian treatment enables analysis of small samples (large p, small n, a crucial problem in bioinformatics, for instance), and rigorous estimation of the degree of dependency and independency. Secondly, the mixture formulation generalizes the method from global linearity to the more reasonable assumption of different kinds of dependencies for different kinds of data. As a third novel extension the method decomposes the variation in the data into shared and data set-specific components.