Fast Kernel Density Estimation with Density Matrices and Random Fourier Features

TL;DR

This paper introduces DMKDE, a novel density estimation method combining quantum-inspired density matrices and random Fourier features, offering efficient, high-dimensional kernel density estimation without memory constraints.

Contribution

The paper presents DMKDE, a new approximation-based KDE method that leverages density matrices and random Fourier features, outperforming existing methods in high-dimensional settings.

Findings

DMKDE performs comparably to state-of-the-art methods on synthetic data.

DMKDE shows advantages in high-dimensional data scenarios.

The code for DMKDE is publicly available as open source.

Abstract

Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method, i.e., it uses the entire training data set for prediction, makes it unsuitable for most current big data applications. Several strategies, such as tree-based or hashing-based estimators, have been proposed to improve the efficiency of the kernel density estimation method. The novel density kernel density estimation method (DMKDE) uses density matrices, a quantum mechanical formalism, and random Fourier features, an explicit kernel approximation, to produce density estimates. This method has its roots in the KDE and can be considered as an approximation method, without its memory-based restriction. In this paper, we systematically evaluate the novel DMKDE algorithm and compare it with other state-of-the-art fast procedures for approximating the kernel density estimation method on different synthetic data sets. Our experimental results show that DMKDE is on par with its competitors for computing density estimates and advantages are shown when performed on high-dimensional data. We have made all the code available as an open source software repository.