Title :
Linear approach to the least-squares multidimensional polynomial fitting
Author_Institution :
Dept. of Inf. Sci., Toho Univ., Funabashi, Japan
Abstract :
It is well known that the least-squares one-dimensional (1-D) polynomial fitting problem is linear. However, multidimensional (M-D) polynomial fitting is often treated as a nonlinear problem. This paper shows that the least-squares M-D polynomial fitting problem is also a linear problem, and proposes a linear method for solving it. Two fitting examples in the 2-D case are given to illustrate the effectiveness of the proposed method
Keywords :
filtering theory; least squares approximations; multidimensional digital filters; polynomials; data interpolation; digital filters; least-squares multidimensional polynomial fitting; linear problem; linear solution method; optimal coefficients; variable filter design; Approximation algorithms; Approximation error; Differential equations; Digital filters; Frequency; Minimization methods; Multidimensional systems; Polynomials; Sampling methods; Vectors;
Conference_Titel :
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN :
0-7803-3676-3
DOI :
10.1109/ICICS.1997.652195
