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Activity Number: 43 - Discovering Homology in Multi-View Data: New Statistical Methods for Data Integration
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #326854 Presentation
Title: Angle Based Joint and Individual Variation Explained
Author(s): J. S. (Steve) Marron and Jan Hannig* and Meilei Jiang and Qing Feng
Companies: University of North Carolina and University of North Carolina and University of North Carolina and Uber
Keywords: data integration; data science; multi-modal; PCA; variation

A major challenge in the age of Big Data is the integration of disparate data types into a data analysis. That is tackled here in the context of data blocks measured on a common set of experimental subjects. This data structure motivates the simultaneous exploration of the joint and individual variation within each data block. This is done here in a way that scales well to large data sets (with blocks of wildly disparate size), using principal angle analysis, careful formulation of the underlying linear algebra, and differing outputs depending on the analytical goals. Ideas are illustrated using mortality, cancer and neuroimaging data sets.

Authors who are presenting talks have a * after their name.

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