Title :
An Explicit Construction Of A Reproducing Gaussian Kernel Hilbert Space
Author :
Xu, Jian-Wu ; Pokharel, Puskal P. ; Jeong, Kyu-Hwa ; Principe, Jose C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL
Abstract :
In this paper, we propose a method to explicitly construct a reproducing kernel Hilbert space (RKHS) associated with a Gaussian kernel by means of polynomial spaces. In contrast to the conventional Mercer´s theorem approach that implicitly defines kernels by an eigendecomposition, the functionals in this reproducing kernel Hilbert space are explicitly constructed and are not necessary orthonormal. We also point out an intriguing connection between this reproducing kernel Hilbert space and a generalized Fock space. We give an experimental result on approximation of the constructed kernel to a Gaussian kernel
Keywords :
Hilbert spaces; approximation theory; eigenvalues and eigenfunctions; learning (artificial intelligence); polynomials; Gaussian kernel Hilbert space; Mercer theorem; eigendecomposition; generalized Fock space; polynomial spaces; Buildings; Hilbert space; Independent component analysis; Kernel; Laboratories; Machine learning; Neural engineering; Polynomials; Principal component analysis; Support vector machines;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661340