Correlation Matrix Spectra: A Tool for Detecting Non-apparent Correlations?

TL;DR

This paper demonstrates that analyzing the eigenvalue spectra of correlation matrices can reveal hidden correlations in models where direct correlation measures appear uninformative.

Contribution

It shows that eigenvalue spectrum analysis can detect non-apparent correlations, providing a more sensitive tool than traditional correlation functions.

Findings

Eigenvalue spectra can reveal hidden correlations

Power-law tails in eigenvalues do not always indicate power-law correlations

Eigenvalue analysis uncovers structure in the TASEP model without visible correlations

Abstract

It has been shown that, if a model displays long-range (power-law) spatial correlations, its equal-time correlation matrix of this model will also have a power law tail in the distribution of its high-lying eigenvalues. The purpose of this letter is to show that the converse is generally incorrect: a power-law tail in the high-lying eigenvalues of the correlation matrix may exist even in the absence of equal-time power law correlations in the original model. We may therefore view the study of the eigenvalue distribution of the correlation matrix as a more powerful tool than the study of correlations, one which may in fact uncover structure, that would otherwise not be apparent. Specifically, we show that in the Totally Asymmetric Simple Exclusion Process, whereas there are no clearly visible correlations in the steady state, the eigenvalues of its correlation matrix exhibit a rich structure which we describe in detail.