Book chapter ยท Conference paper
Empirically driven orthonormal bases for functional data analysis
In implementations of the functional data methods, the effect of the initial choice of an orthonormal basis has not been properly studied. Typically, several standard bases such as Fourier, wavelets, splines, etc. are considered to transform observed functional data and a choice is made without any formal criteria indicating which of the bases is preferable for the initial transformation of the data.
In an attempt to address this issue, we propose a strictly data-driven method of orthonormal basis selection. The method uses B-splines and utilizes recently introduced efficient orthornormal bases called the splinets. The algorithm learns from the data in the machine learning style to efficiently place knots.
The optimality criterion is based on the average (per functional data point) mean square error and is utilized both in the learning algorithms and in comparison studies. The latter indicate efficiency that could be used to analyze responses to a complex physical system.
Language: | English |
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Publisher: | Springer |
Year: | 2021 |
Pages: | 773-783 |
Proceedings: | European Numerical Mathematics and Advanced Applications Conference 2019 |
Series: | Lecture Notes in Computational Science and Engineering |
Journal subtitle: | European Conference, Egmond Aan Zee, the Netherlands, September 30 - October 4 |
ISBN: | 3030558738 , 3030558746 , 9783030558734 and 9783030558741 |
ISSN: | 21977100 and 14397358 |
Types: | Book chapter and Conference paper |
DOI: | 10.1007/978-3-030-55874-1_76 |
ORCIDs: | Nassar, Hiba |