Machine-Learning Methods on Noisy and Sparse Data

Konstantinos Poulinakis, Dimitris Drikakis (), Ioannis W. Kokkinakis and Stephen Michael Spottswood
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Konstantinos Poulinakis: University of Nicosia, Nicosia CY-2417, Cyprus
Dimitris Drikakis: University of Nicosia, Nicosia CY-2417, Cyprus
Ioannis W. Kokkinakis: University of Nicosia, Nicosia CY-2417, Cyprus
Stephen Michael Spottswood: Air Force Research Laboratory, Wright Patterson AFB, Greene County, OH 45433-7402, USA

Mathematics, 2023, vol. 11, issue 1, 1-19

Abstract: Experimental and computational data and field data obtained from measurements are often sparse and noisy. Consequently, interpolating unknown functions under these restrictions to provide accurate predictions is very challenging. This study compares machine-learning methods and cubic splines on the sparsity of training data they can handle, especially when training samples are noisy. We compare deviation from a true function f using the mean square error, signal-to-noise ratio and the Pearson R 2 coefficient. We show that, given very sparse data, cubic splines constitute a more precise interpolation method than deep neural networks and multivariate adaptive regression splines. In contrast, machine-learning models are robust to noise and can outperform splines after a training data threshold is met. Our study aims to provide a general framework for interpolating one-dimensional signals, often the result of complex scientific simulations or laboratory experiments.

Keywords: machine learning; deep neural networks; MARS; splines; interpolation; feedforward neural networks; noisy data; sparse data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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