Contrastive Functional Principal Components Analysis.

ABSTRACT: As functional data assumes a central role in contemporary data analysis, the search for meaningful dimension reduction becomes critical due to its inherent infinite-dimensional structure. Traditional methods, such as Functional Principal Component Analysis (FPCA), adeptly explore the overarching structures within the functional data. However, these methods may not sufficiently identify low-dimensional representations that are specific or enriched in a foreground dataset (case or treatment group) relative to a background dataset (control group). This limitation becomes critical in scenarios where the foreground dataset, such as a specific treatment group in biomedical applications, contains unique patterns or trends that are not as pronounced in the background dataset. Addressing this gap,

SUBMITTER: Zhang E 

PROVIDER: S-EPMC12768335 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature