Article | Proceedings of SIGRAD 2016, May 23rd and 24th, Visby, Sweden | A Radial Basis Function Approximation for Large Datasets
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Title:
A Radial Basis Function Approximation for Large Datasets
Author:
Zuzana Majdisova: Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Czech Republic Vaclav Skala: Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Czech Republic
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Full text (pdf)
Year:
2016
Conference:
Proceedings of SIGRAD 2016, May 23rd and 24th, Visby, Sweden
Issue:
127
Article no.:
002
Pages:
9-14
No. of pages:
6
Publication type:
Abstract and Fulltext
Published:
2016-05-30
ISBN:
978-91-7685-731-1
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered datasets in d-dimensional space. It is non-separable approximation, as it is based on a distance between two points. This method leads to a solution of overdetermined linear system of equations. In this paper a new approach to the RBF approximation of large datasets is introduced and experimental results for different real datasets and different RBFs are presented with respect to the accuracy of computation. The proposed approach uses symmetry of matrix and partitioning matrix into blocks.

Keywords: Radial basis function RBF approximation LiDAR data

Proceedings of SIGRAD 2016, May 23rd and 24th, Visby, Sweden

Author:
Zuzana Majdisova, Vaclav Skala
Title:
A Radial Basis Function Approximation for Large Datasets
References:

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Proceedings of SIGRAD 2016, May 23rd and 24th, Visby, Sweden

Author:
Zuzana Majdisova, Vaclav Skala
Title:
A Radial Basis Function Approximation for Large Datasets
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