The full research paper is available in the journal: https://lnkd.in/ghCZFMp
Abstract: Kernel density estimation (KDE) is an important method in nonparametric learning, but it is highly sensitive to the bandwidth parameter. The existing techniques tend to under smooth or over smooth the density estimation. Especially when data is noisy, which is a common trait of real-world data sources. This paper proposes a fully data driven approach to avoid under smoothness and over smoothness in density estimation. This paper uses a cost function to achieve optimal bandwidth by evaluating a weighted error metric, where the weight function ensures low bias and low variance during learning. The density estimation uses the computationally efficient Fast Fourier Transform (FFT) to estimate the univariate Gaussian kernel density. Thus bringing the computation cost of a single density evaluation from O(n2) to O(m log(m)), where m n and m being the grid points of FFT. Based upon simulation results this paper significantly outperforms the de-facto classical methods and the more recent papers over a standard benchmark dataset. The results specially shines apart from the recent and classical approaches when data contains significant noise.