Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression

Published: 06 Oct 2022, Last Modified: 30 Jun 2023Accepted by TMLREveryoneRevisionsBibTeX
Authors that are also TMLR Expert Reviewers: ~Jeff_Phillips1
Abstract: We study a localized version of kernel ridge regression that can continuously, smoothly interpolate the underlying function values which are highly non-linear with observed data points. This new method can deal with the data of which (a) local density is highly uneven and (b) the function values change dramatically in certain small but unknown regions. By introducing a new rank-based interpolation scheme, the interpolated values provided by our local method continuously vary with query points. Our method is scalable by avoiding the full matrix inverse, compared with traditional kernel ridge regression.
Certifications: Expert Certification
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Makoto_Yamada3
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 281