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Activity Number: 34 - Advanced Methods in Statistical Learning
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #322662
Title: Data Integration via Analysis of Subspaces (DIVAS)
Author(s): Jack B. Prothero* and Jan Hannig and J. S. (Steve) Marron and Quoc Tran-Dinh and Meilei Jiang
Companies: National Institute of Standards and Technology and University of Noerth Carolina at Chapel Hill and UNC and University of North Carolina Chapel Hill and Meta
Keywords: Data Integration; Principal Angles; Multi-Block; Partially-Shared
Abstract:

Modern data collection in bioinformatics and other big-data paradigms often incorporates traits derived from multiple different points of view of the observations. We call this data multi-view or multi-block data. The emergent field of data integration develops and applies new methods for studying multi-block data and identifying how different data blocks relate and differ. One major frontier in contemporary data integration research is methodology that can identify partially-shared structure between sub-collections of data blocks. This work presents our new method on this frontier: Data Integration Via Analysis of Subspaces (DIVAS). DIVAS combines new insights in angular subspace perturbation theory with recent developments in matrix signal processing and convex-concave optimization into one algorithm for parsing partially shared structure. Our novel approach based on principal angles between subspaces provides built-in inference on the results of the analysis, and is effective even in high-dimension-low-sample-size (HDLSS) situations.


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

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