Prof. Samuel Kaski

Aalto University and University of Helsinki, Finland

Bio: Samuel Kaski is Academy (research) Professor of the Academy of Finland, Professor of Computer Science at Aalto University, and Director of Finnish Center of Excellence in Computational Inference Research COIN. His field is probabilistic machine learning, with applications involving multiple data sources in interactive information retrieval, data visualization, health and biology.

Bayesian factorization of multiple data sources

Abstract: 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.