Operator Reproducing Kernel Hilbert Spaces

Abstract

Motivated by the need of processing functional-valued data, or more general, operatorvalued data, we introduce the notion of the operator reproducing kernel Hilbert space (ORKHS). This space admits a unique operator reproducing kernel which reproduces a family of continuous linear operators on the space. The theory of ORKHSs and the associated operator reproducing kernels are established. A special class of ORKHSs, known as the perfect ORKHSs, are studied, which reproduce the family of the standard point-evaluation operators and at the same time another different family of continuous linear operators. The perfect ORKHSs are characterized in terms of features, especially for those with respect to integral operators. In particular, several specific examples of the perfect ORKHSs are presented. We apply the theory of ORKHSs to sampling and regularized learning, where operator-valued data are considered. Specifically, a general complete reconstruction formula from linear operators values is established in the framework of ORKHSs. The average sampling and the reconstruction of vector-valued functions are considered in specific ORKHSs. We also investigate in the ORKHSs setting the regularized learning schemes, which learn a target element from operator-valued data. The desired representer theorems of the learning problems are established to demonstrate the key roles played by the ORKHSs and the operator reproducing kernels in machine learning from operator-valued data. We finally point out that the continuity of linear operators, used to obtain the operator-valued data, on an ORKHS is necessary for the stability of the numerical reconstruction algorithm using the resulting data.