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- Title
Machine-Learning Methods on Noisy and Sparse Data.
- Authors
Poulinakis, Konstantinos; Drikakis, Dimitris; Kokkinakis, Ioannis W.; Spottswood, Stephen Michael
- 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.
- Subjects
ARTIFICIAL neural networks; FEEDFORWARD neural networks; SIGNAL-to-noise ratio; MACHINE learning
- Publication
Mathematics (2227-7390), 2023, Vol 11, Issue 1, p236
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math11010236