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Activity Number: 335 - ASA Biometrics Section JSM Travel Awards (II)
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #327122 Presentation
Title: Structural Learning and Integrative Decomposition of Multi-View Data
Author(s): Irina Gaynanova* and Gen Li
Companies: Texas A&M Univeristy and Columbia University
Keywords: data integration; dimension reduction; multiblock methods; principal component analysis; structured sparsity

The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. These models represent each data view via shared and individual components, and have been successfully applied for exploratory dimension reduction, association analysis between the views, and consensus clustering. Despite these advances, there remain challenges in modeling partially-shared components, and identifying the number of components of each type (shared/partially-shared/individual). We formulate a novel model called SLIDE that directly incorporates partially-shared structures. We prove the existence of SLIDE decomposition and explicitly characterize the identifiability conditions. The proposed model fitting and selection techniques allow for joint identification of the number of components of each type, in contrast to existing sequential approaches. In our empirical studies, SLIDE demonstrates excellent performance in both signal estimation and component selection. We further illustrate the methodology on the breast cancer data from The Cancer Genome Atlas repository.

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

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