An introduction to variational methods for graphical models
Learning in graphical models
A unifying review of linear Gaussian models
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Expectation Propagation for approximate Bayesian inference
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
Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 24th international conference on Machine learning
Variational Bayesian Approach to Canonical Correlation Analysis
IEEE Transactions on Neural Networks
Matching samples of multiple views
Data Mining and Knowledge Discovery
Extracting coactivated features from multiple data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Probabilistic proactive timeline browser
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Sparse structured probabilistic projections for factorized latent spaces
Proceedings of the 20th ACM international conference on Information and knowledge management
Dynamic probabilistic CCA for analysis of affective behaviour
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Bayesian Canonical correlation analysis
The Journal of Machine Learning Research
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We study data fusion under the assumption that data source-specific variation is irrelevant and only shared variation is relevant. Traditionally the shared variation has been sought by maximizing a dependency measure, such as correlation of linear projections in canonical correlation analysis (CCA). In this traditional framework it is hard to tackle overfitting and model order selection, and thus we turn to probabilistic generative modeling which makes all tools of Bayesian inference applicable. We introduce a family of probabilistic models for the same task, and present conditions under which they seek dependency. We show that probabilistic CCA is a special case of the model family, and derive a new dependency-seeking clustering algorithm as another example. The solution is computed with variational Bayes.