Nonlinear multi-study factor analysis
Published in arXiv (Cornell University) • Jan 26, 2026
NobleIDNI6P66W61R46S72
Authors:,
Gemma E. Moran
Anandi Krishnan
Abstract
High-dimensional data often exhibit variation that can be captured by lower dimensional factors. For high-dimensional data from multiple studies or environments, one goal is to understand which underlying factors are common to all studies, and which factors are study or environment-specific. As a pa...
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