UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Kernel Mean Shrinkage Estimators

Muandet, K; Sriperumbudur, B; Fukumizu, K; Gretton, A; Schölkopf, B; (2016) Kernel Mean Shrinkage Estimators. Journal of Machine Learning Research , 17 , Article 48. Green open access

[thumbnail of Muandet_Kernel_Mean_Shrinkage_Estimators_VoR.pdf]
Preview
Text
Muandet_Kernel_Mean_Shrinkage_Estimators_VoR.pdf

Download (648kB) | Preview

Abstract

A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern kernel methods that rely on embedding probability distributions in RKHSs. Given a finite sample, an empirical average has been used commonly as a standard estimator of the true kernel mean. Despite a widespread use of this estimator, we show that it can be improved thanks to the well-known Stein phenomenon. We propose a new family of estimators called kernel mean shrinkage estimators (KMSEs), which benefit from both theoretical justifications and good empirical performance. The results demonstrate that the proposed estimators outperform the standard one, especially in a "large d, small n" paradigm.

Type: Article
Title: Kernel Mean Shrinkage Estimators
Open access status: An open access version is available from UCL Discovery
Publisher version: http://jmlr.org/papers/v17/14-195.html
Language: English
Additional information: Copyright © The authors. Licensed under a Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0).
Keywords: covariance operator, James-Stein estimators, kernel methods, kernel mean, shrinkage estimators, Stein effect, Tikhonov regularization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/1522727
Downloads since deposit
25Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item