Double-matched matrix decomposition for multi-view data

• URL: http://arxiv.org/abs/2105.03396v1
• Date: Fri, 7 May 2021 17:09:57 GMT
• Title: Double-matched matrix decomposition for multi-view data
• Authors: Dongbang Yuan and Irina Gaynanova
• Abstract summary: We consider the problem of extracting joint and individual signals from multi-view data, that is data collected from different sources on matched samples. Our proposed double-matched matrix decomposition allows to simultaneously extract joint and individual signals across subjects. We apply our method to data from the English Premier League soccer matches, and find joint and individual multi-view signals that align with domain specific knowledge.
• Score: 0.6091702876917281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of extracting joint and individual signals from multi-view data, that is data collected from different sources on matched samples. While existing methods for multi-view data decomposition explore single matching of data by samples, we focus on double-matched multi-view data (matched by both samples and source features). Our motivating example is the miRNA data collected from both primary tumor and normal tissues of the same subjects; the measurements from two tissues are thus matched both by subjects and by miRNAs. Our proposed double-matched matrix decomposition allows to simultaneously extract joint and individual signals across subjects, as well as joint and individual signals across miRNAs. Our estimation approach takes advantage of double-matching by formulating a new type of optimization problem with explicit row space and column space constraints, for which we develop an efficient iterative algorithm. Numerical studies indicate that taking advantage of double-matching leads to superior signal estimation performance compared to existing multi-view data decomposition based on single-matching. We apply our method to miRNA data as well as data from the English Premier League soccer matches, and find joint and individual multi-view signals that align with domain specific knowledge.

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