Kernel-Based Regressors Equivalent to Stochastic Affine Estimators
- 1 January 2022
- journal article
- research article
- Published by Institute of Electronics, Information and Communications Engineers (IEICE) in IEICE Transactions on Information and Systems
- Vol. E105.D (1), 116-122
- https://doi.org/10.1587/transinf.2021edp7156
Abstract
The solution of the ordinary kernel ridge regression, based on the squared loss function and the squared norm-based regularizer, can be easily interpreted as a stochastic linear estimator by considering the autocorrelation prior for an unknown true function. As is well known, a stochastic affine estimator is one of the simplest extensions of the stochastic linear estimator. However, its corresponding kernel regression problem is not revealed so far. In this paper, we give a formulation of the kernel regression problem, whose solution is reduced to a stochastic affine estimator, and also give interpretations of the formulation.Keywords
This publication has 1 reference indexed in Scilit:
- Kernel-Induced Sampling TheoremIEEE Transactions on Signal Processing, 2010