Analyzing Functional Data of Two-Dimensional Arguments using Tensor Spline Orthonormal Bases
(2024) STAN40 20241Department 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:
http://lup.lub.lu.se/student-papers/record/9159546
- author
- Wikstrand, Freja LU
- supervisor
- organization
- course
- STAN40 20241
- year
- 2024
- 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}}, }