Kernel-Induced Sampling Theorem

スポンサーリンク

概要

• 論文の詳細を見る

• A perfect reconstruction of functions in a reproducing kernel Hilbert space from a given set of sampling points is discussed. A necessary and sufficient condition for the corresponding reproducing kernel and the given set of sampling points to perfectly recover the functions is obtained in this paper. The key idea of our work is adopting the reproducing kernel Hilbert space corresponding to the Gramian matrix of the kernel and the given set of sampling points as the range space of a sampling operator and considering the orthogonal projector, defined via the range space, onto the closed linear subspace spanned by the kernel functions corresponding to the given sampling points. We also give an error analysis of a reconstructed function by incomplete sampling points.

著者

• Miyakoshi Masaaki

  Division Of Systems And Information Engineering Graduate School Of Engineering Hokkaido University

関連論文

• Kernel-Induced Sampling Theorem
• Integrated kernels and their properties
• A Unified Framework of Subspace Identification for D.O.A. Estimation(Engineering Acoustics)
• On Formulations and Solutions in Linear Image Restoration Problems(Image)
• Choosing the Parameter of Image Restoration Filters by Modified Subspace Information Criterion
• The Family of Regularized Parametric Projection Filters for Digital Image Restoration
• A method of identifying influential data in fuzzy clustering
• The Family of Parametric Projection Filters and Its Properties for Perturbation

スポンサーリンク