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Analyzing Functional Data of Two-Dimensional Arguments using Tensor Spline Orthonormal Bases

Wikstrand, Freja LU (2024) STAN40 20241
Department of Statistics
Abstract
This thesis establishes a theoretical framework for constructing orthonormal tensor spline bases, to transform two-dimensional functions into a coherent set of coefficients, for easier analysis of functional data. Through empirical testing on image datasets, the method showcases promising results, underscoring its utility in pattern recognition tasks. This research lays a foundation for further exploration into advanced data analysis techniques, with implications extending to multidimensional functional representations and computational modelling.
Please use this url to cite or link to this publication:
author
Wikstrand, Freja LU
supervisor
organization
course
STAN40 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
Functional Data, Splines, Tensor Splines, Orthonormal Bases, Splinets
language
English
id
9159546
date added to LUP
2024-06-17 14:30:46
date last changed
2024-06-17 14:30:46
@misc{9159546,
  abstract     = {{This thesis establishes a theoretical framework for constructing orthonormal tensor spline bases, to transform two-dimensional functions into a coherent set of coefficients, for easier analysis of functional data. Through empirical testing on image datasets, the method showcases promising results, underscoring its utility in pattern recognition tasks. This research lays a foundation for further exploration into advanced data analysis techniques, with implications extending to multidimensional functional representations and computational modelling.}},
  author       = {{Wikstrand, Freja}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Analyzing Functional Data of Two-Dimensional Arguments using Tensor Spline Orthonormal Bases}},
  year         = {{2024}},
}