Non-Gaussian Component Analysis via Lattice Basis Reduction

TL;DR

This paper presents a new efficient algorithm for Non-Gaussian Component Analysis that works effectively even when the non-Gaussian distribution is discrete, using lattice basis reduction techniques.

Contribution

It introduces a novel algorithm leveraging lattice basis reduction to solve NGCA for discrete distributions, overcoming previous limitations.

Findings

Efficient algorithm for NGCA with discrete distributions.

Breaks the information-computation tradeoff barrier in this setting.

Uses LLL lattice basis reduction as a key tool.

Abstract

Non-Gaussian Component Analysis (NGCA) is the following distribution learning problem: Given i.i.d. samples from a distribution on $\mathbb{R}^d$ that is non-gaussian in a hidden direction $v$ and an independent standard Gaussian in the orthogonal directions, the goal is to approximate the hidden direction $v$. Prior work \cite{DKS17-sq} provided formal evidence for the existence of an information-computation tradeoff for NGCA under appropriate moment-matching conditions on the univariate non-gaussian distribution $A$. The latter result does not apply when the distribution $A$ is discrete. A natural question is whether information-computation tradeoffs persist in this setting. In this paper, we answer this question in the negative by obtaining a sample and computationally efficient algorithm for NGCA in the regime that $A$ is discrete or nearly discrete, in a well-defined technical sense. The key tool leveraged in our algorithm is the LLL method \cite{LLL82} for lattice basis reduction.