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Kernel-Based Regressors Equivalent to Stochastic Affine Estimators
| Title: | Kernel-Based Regressors Equivalent to Stochastic Affine Estimators |
| Authors: | Tanaka, Akira Browse this author →KAKEN DB | | Nakamura, Masanari Browse this author →KAKEN DB | | Imai, Hideyuki Browse this author →KAKEN DB |
| Keywords: | kernel regression | | autocorrelation prior | | linear estimators | | affine estimators | | optimization criterion |
| Issue Date: | 1-Jan-2022 |
| Publisher: | IEICE - Institute of the Electronics, Information and Communication Engineers |
| Journal Title: | IEICE transactions on information and systems |
| Volume: | E105D |
| Issue: | 1 |
| Start Page: | 116 |
| End Page: | 122 |
| Publisher DOI: | 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. |
| Rights: | Copyright ©2022 The Institute of Electronics, Information and Communication Engineers |
| Type: | article |
| URI: | http://hdl.handle.net/2115/84450 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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