Stable Bases for Kernel Based Methods
by Maryam Pazouki
Date of Examination:2012-06-13
Date of issue:2013-03-18
Advisor:Prof. Dr. Robert Schaback
Referee:Prof. Dr. Gert Lube
Referee:Prof. Dr. Stephan Waack
Persistent Address:
http://dx.doi.org/10.53846/goediss-3777
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Abstract
English
Kernels K arise in many contexts, including approximation, surface reconstruction, numerical analysis of fluid-structure interactions, and geostatistics. In particular, the span of translates K(.,x) for scattered centers xj forms a very useful trial space for these applications. However, the basis of these functions usually is very ill--conditioned, but due to results of S. De Marchi and R. Schaback, the interpolants to data on X, when viewed as functions, are rather stable. This leads us to consider different data--dependent bases. We concentrate to introduce a new technique to came up with a number of useful bases having special properties in kernel spaces for multivariate interpolation on a set of scattered data. This work provides a variety of different bases based on factorizations of the kernel matrix. These bases differ in their stability, orthogonality, adaptivity, duality, and computational efficiency properties. Special emphasis is given to the "Newton'' basis arising from a pivoted Cholesky factorization. It turns out to be stable and computationally cheap while being orthonormal in the "native'' Hilbert space of the kernel. There are efficient adaptive algorithms for calculating the Newton basis along the lines of Orthogonal Matching Pursuit. This work extends to the important case of Conditionally positive definite kernels arise in many contexts including approximation function algorithms, surface reconstruction, numerical analysis of fluid-structure interactions, computer experiment, geostatistics. They are intended to generalize the well known positive definite kernel cases to the important case of conditionally positive kernels such as thin--plate splines or polyharmonic kernels. The goal is to construct well-behaving bases for interpolation on a finite set X in R by translates K(.,x) for x in X of a fixed kernel K which is conditionally positive definite of order m>0. Particularly interesting cases are bases of Lagrange or Newton type, and bases which are orthogonal or orthonormal either discretely (i.e. via their function values on X) or as elements of the underlying "native'' space for the given kernel, which is a direct product of a Hilbert space with the space Pmd of d-variate polynomials of order up to m. Eventually, we verify that using reduced kernel, a positive definite Kernel, leads to the same Newton Basis that provided by adaptive method, by these foundations, the conditionally positive definite case turn out by adding polynomial part, that proposed by Extended Kernels.
Keywords: positive definite Kernel, Newton Basis, Conditionally positive definite kernels; positive definite Kernel, Newton Basis, Conditionally positive definite kernels; positive definite Kernel, Newton Basis, Conditionally positive definite kernels
