On the Inclusion Relation of Reproducing Kernel Hilbert Spaces

To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of reproducing kernels. Finally, we briefly discuss the special inclusion with a norm equivalence.

[1]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[2]  N. Aronszajn Theory of Reproducing Kernels. , 1950 .

[3]  Michael I. Jordan,et al.  Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces , 2004, J. Mach. Learn. Res..

[4]  A. Berlinet,et al.  Reproducing kernel Hilbert spaces in probability and statistics , 2004 .

[5]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[6]  Felipe Cucker,et al.  On the mathematical foundations of learning , 2001 .

[7]  I. J. Schoenberg Positive definite functions on spheres , 1942 .

[8]  C. Micchelli,et al.  Functions that preserve families of positive semidefinite matrices , 1995 .

[9]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Jun Zhang,et al.  Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products☆ , 2011 .

[12]  Fazhan Geng,et al.  Solving singular two-point boundary value problem in reproducing kernel space , 2007 .

[13]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[14]  Yuesheng Xu,et al.  Refinable Kernels , 2007, J. Mach. Learn. Res..

[15]  S. Bochner,et al.  Lectures on Fourier integrals : with an author's supplement on monotonic functions, Stieltjes integrals, and harmonic analysis , 1959 .

[16]  Carsten Franke,et al.  Solving partial differential equations by collocation using radial basis functions , 1998, Appl. Math. Comput..

[17]  Roland Opfer,et al.  Multiscale kernels , 2006, Adv. Comput. Math..

[18]  Yuesheng Xu,et al.  Refinement of Reproducing Kernels , 2009, J. Mach. Learn. Res..

[19]  N. Donald Ylvisaker On Linear Estimation for Regression Problems on Time Series , 1962 .

[20]  I. J. Schoenberg Metric spaces and completely monotone functions , 1938 .

[21]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[22]  J. Mercer Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .

[23]  M. Zuhair Nashed,et al.  General sampling theorems for functions in reproducing kernel Hilbert spaces , 1991, Math. Control. Signals Syst..

[24]  Barbara Zwicknagl,et al.  F ¨ Ur Mathematik in Den Naturwissenschaften Leipzig Power Series Kernels Power Series Kernels , 2022 .

[25]  J. Cooper SINGULAR INTEGRALS AND DIFFERENTIABILITY PROPERTIES OF FUNCTIONS , 1973 .

[26]  Bernhard Schölkopf,et al.  Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..

[27]  Don R. Hush,et al.  An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels , 2006, IEEE Transactions on Information Theory.

[28]  M. Driscoll The reproducing kernel Hilbert space structure of the sample paths of a Gaussian process , 1973 .