Analysis via Orthonormal Systems in Reproducing Kernel Hilbert $C^*$-Modules and Applications

2 Mar 2020Yuka HashimotoIsao IshikawaMasahiro IkedaFuyuta KomuraTakeshi KatsuraYoshinobu Kawahara

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS). In this paper, we propose a novel data analysis framework with reproducing kernel Hilbert $C^*$-module (RKHM), which is another generalization of RKHS than vector-valued RKHS (vv-RKHS)... (read more)

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