Inhomogeneous Submatrix Detection

TL;DR

This paper investigates the detection of multiple inhomogeneous hidden submatrices within large Gaussian matrices, establishing theoretical detection limits and proposing algorithms that approach these bounds.

Contribution

It introduces a comprehensive analysis of detection limits for inhomogeneous submatrices under various models and placement regimes, with matching algorithms.

Findings

Established information-theoretic lower bounds for detection.

Designed algorithms matching these bounds up to logarithmic factors.

Analyzed detection in both mean-shift and variance-shift models.

Abstract

In this paper, we study the problem of detecting multiple hidden submatrices in a large Gaussian random matrix when the planted signal is inhomogeneous across entries. Under the null hypothesis, the observed matrix has independent and identically distributed standard normal entries. Under the alternative, there exist several planted submatrices whose entries deviate from the background in one of two ways: in the mean-shift model, planted entries (templates) have nonzero and possibly varying means; in the variance-shift model, planted entries have inflated and possibly varying variances. We consider two placement regimes for the planted submatrices. In the first, the row and column index sets are arbitrary. Motivated by scientific applications, in the second regime the row and column indices are restricted to be consecutive. For both alternatives and both placement regimes, we analyze the statistical limits of detection by proving information-theoretic lower bounds and by designing algorithms that match these bounds up to logarithmic factors, for a wide family of templates.