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