PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generative models that discover dependencies between data sets
Arto Klami and Samuel Kaski
In: MLSP 2006, 6-8 Sep 2006, Maynooth, Ireland.


We develop models for a kind of data fusion task: Combine multiple data sources under the assumption that data set specific variation is irrelevant and only between-data variation is relevant. We extend a recent generative modeling interpretation of Canonical Correlation Analysis (CCA), a traditional linear method applicable to this task, in a way which allows generalization to other types of models. The generative formulation makes all standard tools of Bayesian inference applicable. We finally introduce new dependency-seeking clustering models that outperform standard generative clustering models in their task.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Multimodal Integration
Theory & Algorithms
ID Code:2211
Deposited By:Arto Klami
Deposited On:28 September 2006