Supervised low-rank approximation of high-dimensional multivariate functional data via tensor decomposition

TL;DR

This paper introduces SupFTSVD, a supervised tensor decomposition method that effectively reduces high-dimensional functional data by incorporating auxiliary information and temporal structure, improving interpretability and accuracy.

Contribution

The paper presents a novel supervised tensor SVD method that leverages auxiliary information and temporal data for better dimensionality reduction of high-dimensional functional tensors.

Findings

Improved tensor approximation accuracy in simulations

Enhanced loading estimation with auxiliary information

Revealed meaningful biological patterns in microbiome studies

Abstract

Motivated by the challenges of analyzing high-dimensional ($p \gg n$) sequencing data from longitudinal microbiome studies, where samples are collected at multiple time points from each subject, we propose supervised functional tensor singular value decomposition (SupFTSVD), a novel dimensionality reduction method that leverages auxiliary information in the dimensionality reduction of high-dimensional functional tensors. Although multivariate functional principal component analysis is a natural choice for dimensionality reduction of multivariate functional data, it becomes computationally burdensome in high-dimensional settings. Low-rank tensor decomposition is a feasible alternative and has gained popularity in recent literature, but existing methods in this realm are often incapable of simultaneously utilizing the temporal structure of the data and subject-level auxiliary information. SupFTSVD overcomes these limitations by generating low-rank representations of high-dimensional functional tensors while incorporating subject-level auxiliary information and accounting for the functional nature of the data. Moreover, SupFTSVD produces low-dimensional representations of subjects, features, and time, as well as subject-specific trajectories, providing valuable insights into the biological significance of variations within the data. In simulation studies, we demonstrate that our method achieves notable improvement in tensor approximation accuracy and loading estimation by utilizing auxiliary information. Finally, we applied SupFTSVD to two longitudinal microbiome studies where biologically meaningful patterns in the data were revealed.