Sparse multiway canonical correlation analysis for multimodal stroke recovery data

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Conventional canonical correlation analysis (CCA) measures the association between two datasets and identifies relevant contributors. However, it encounters issues with execution and interpretation when the sample size is smaller than the number of variables or there are more than two datasets. Our motivating example is a stroke-related clinical study on pigs. The data are multimodal and consist of measurements taken at multiple time points and have many more variables than observations. This study aims to uncover important biomarkers and stroke recovery patterns based on physiological changes. To address the issues in the data, we develop two sparse CCA methods for multiple datasets. Various simulated examples are used to illustrate and contrast the performance of the proposed methods with that of the existing methods. In analyzing the pig stroke data, we apply the proposed sparse CCA methods along with dimension reduction techniques, interpret the recovery patterns, and identify influential variables in recovery.
Publisher
WILEY
Issue Date
2024-03
Language
English
Article Type
Article
Citation

BIOMETRICAL JOURNAL, v.66, no.2

ISSN
0323-3847
DOI
10.1002/bimj.202300037
URI
http://hdl.handle.net/10203/319689
Appears in Collection
MA-Journal Papers(저널논문)
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