Explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time

TL;DR

This paper derandomizes Valiant's subquadratic algorithm for detecting outlier correlations in binary data using explicit correlation amplifiers based on zigzag-product expanders, achieving deterministic subquadratic performance.

Contribution

It introduces explicit correlation amplifiers constructed via zigzag-product expanders, enabling deterministic subquadratic algorithms for outlier correlation detection.

Findings

Achieves deterministic subquadratic time for outlier correlation detection.

Constructs correlation amplifiers with explicit mathematical properties.

Provides bounds on correlation amplification for binary vectors.

Abstract

We derandomize G. Valiant's [J. ACM 62 (2015) Art. 13] subquadratic-time algorithm for finding outlier correlations in binary data. Our derandomized algorithm gives deterministic subquadratic scaling essentially for the same parameter range as Valiant's randomized algorithm, but the precise constants we save over quadratic scaling are more modest. Our main technical tool for derandomization is an explicit family of correlation amplifiers built via a family of zigzag-product expanders in Reingold, Vadhan, and Wigderson [Ann. of Math. 155 (2002) 157--187]. We say that a function $f:\{-1,1\}^d\rightarrow\{-1,1\}^D$ is a correlation amplifier with threshold $0\leq\tau\leq 1$, error $\gamma\geq 1$, and strength $p$ an even positive integer if for all pairs of vectors $x,y\in\{-1,1\}^d$ it holds that (i) $|\langle x,y\rangle|<\tau d$ implies $|\langle f(x),f(y)\rangle|\leq(\tau\gamma)^pD$; and (ii) $|\langle x,y\rangle|\geq\tau d$ implies $\bigl(\frac{\langle x,y\rangle}{\gamma d}\bigr)^pD \leq\langle f(x),f(y)\rangle\leq \bigl(\frac{\gamma\langle x,y\rangle}{d}\bigr)^pD$.