Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 356 - Statistical Learning: Methods and Applications
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313827
Title: Common and Distinctive Pattern Analysis Between High-Dimensional Data Sets
Author(s): Hai Shu* and Zhe Qu
Companies: New York University and Tulane University
Keywords: Canonical variable; Data integration; Graph matching; Mixing channel; Principal vector; Common and distinctive patterns

A representative model in integrative analysis of two high-dimensional data types is to decompose each data matrix into a low-rank common matrix generated by latent factors shared across data types, a low-rank distinctive matrix corresponding to each data type, and an additive noise matrix. Existing decomposition methods claim that their common matrices capture the common pattern of the two data types. However, their so-called common pattern only denotes the common latent factors but ignores the common information between the two coefficient matrices of these latent factors. We propose a novel method, called the common and distinctive pattern analysis, which appropriately defines the two patterns by further incorporating the common and distinctive information of the coefficient matrices. A consistent estimation approach is developed for high-dimensional settings, and shows reasonably good finite-sample performance in simulations. We illustrate the superiority of proposed method over the state-of-the-art by real-world data examples obtained from Human Connectome Project and The Cancer Genome Atlas.

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

Back to the full JSM 2020 program