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Bayesian factorization of multiple data sources

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An increasingly common data analysis task is to factorize multiple data matrices together. The goal can be to borrow strength from related data sources for missing value imputation or prediction, or to find out what is shared between different sources and what is unique in each. I will discuss an extension of factor analysis to this task, group factor analysis GFA , and its extension from analysis of multiple coupled matrices to multiple coupled tensors and matrices. I will pick examples from molecular medicine and brain data analysis.

This talk is part of the CL-CompBio series.

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