Over the past decades, the increasing dimensionality of data has increased the need for effective data decomposition methods. Existing approaches, however, often rely on linear models or lack sufficient interpretability or flexibility. To address this issue, we introduce a novel nonlinear decomposition technique called the principal nested submanifolds, which builds on the foundational concepts of principal component analysis. This method exploits the local geometric information of data sets by projecting samples onto a series of nested principal submanifolds with progressively decreasing dimensions. It effectively isolates complex information within the data in a backward stepwise manner by targeting variations associated with smaller eigenvalues in local covariance matrices. The fitted structures are smooth nested manifolds rather than linear or special-shape subspaces. Simulation studies and an analysis of single-cell RNA-sequencing data illustrate how the method summarizes locally varying nonlinear structure while retaining a compatible hierarchy across dimensions. It provides flexible subspace constraints that facilitate the extraction of data components and noise reduction. This approach advances the non-Euclidean statistical analysis of data with low-dimensional intrinsic structure within Euclidean spaces and offers a complementary perspective for high-dimensional noisy data sets in fields such as bioinformatics and machine learning.

翻译：暂无翻译