Distribution of singular values in large sample cross-covariance matrices

Fuente: arXiv
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Auteurs principaux: Swain, Arabind, Ridout, Sean Alexander, Nemenman, Ilya
Format: Preprint
Publié: 2025
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author Swain, Arabind
Ridout, Sean Alexander
Nemenman, Ilya
author_facet Swain, Arabind
Ridout, Sean Alexander
Nemenman, Ilya
contents For two large matrices ${\mathbf X}$ and ${\mathbf Y}$ with Gaussian i.i.d.\ entries and dimensions $T\times N_X$ and $T\times N_Y$, respectively, we derive the probability distribution of the singular values of $\mathbf{X}^T \mathbf{Y}$ in different parameter regimes. This extends the Marchenko-Pastur result for the distribution of eigenvalues of empirical sample covariance matrices to singular values of empirical cross-covariances. Our results will help to establish statistical significance of cross-correlations in many data-science applications.
format Preprint
id arxiv_https___arxiv_org_abs_2502_05254
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Distribution of singular values in large sample cross-covariance matrices
Swain, Arabind
Ridout, Sean Alexander
Nemenman, Ilya
Statistics Theory
Disordered Systems and Neural Networks
Data Analysis, Statistics and Probability
For two large matrices ${\mathbf X}$ and ${\mathbf Y}$ with Gaussian i.i.d.\ entries and dimensions $T\times N_X$ and $T\times N_Y$, respectively, we derive the probability distribution of the singular values of $\mathbf{X}^T \mathbf{Y}$ in different parameter regimes. This extends the Marchenko-Pastur result for the distribution of eigenvalues of empirical sample covariance matrices to singular values of empirical cross-covariances. Our results will help to establish statistical significance of cross-correlations in many data-science applications.
title Distribution of singular values in large sample cross-covariance matrices
topic Statistics Theory
Disordered Systems and Neural Networks
Data Analysis, Statistics and Probability
url https://arxiv.org/abs/2502.05254