Open AccessBandwidth selection for kernel density estimation using Fourier domain constraints
Author(s) -
Suhre Alexander,
Arikan Orhan,
Cetin Ahmed Enis
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2015.0076
Subject(s) - smoothing , kernel density estimation , bandwidth (computing) , variable kernel density estimation , computer science , density estimation , kernel (algebra) , algorithm , fourier transform , multivariate kernel density estimation , parametric statistics , mathematical optimization , prior probability , mathematics , kernel method , artificial intelligence , statistics , telecommunications , mathematical analysis , combinatorics , estimator , support vector machine , computer vision , bayesian probability
Kernel density estimation (KDE) is widely‐used for non‐parametric estimation of an underlying density from data. The performance of KDE is mainly dependent on the bandwidth parameter of the kernel. This study presents an alternative method of estimating the bandwidth by incorporating sparsity priors in the Fourier transform domain. By using cross‐validation (CV) together with an l 1 constraint, the proposed method significantly reduces the under‐smoothing effect of traditional CV methods. A solution for all free parameters in the minimisation is proposed, such that the algorithm does not need any additional parameter tuning. Simulation results indicate that the new approach is able to outperform classical and more recent approaches over a set of distributions of interest.