Search Machine Learning Repository:
**Kernel Mean Estimation and Stein Effect**

**Authors:** *Krikamol Muandet*, *Kenji Fukumizu*, *Bharath Sriperumbudur*, *Arthur Gretton* and *Bernhard Schölkopf*

**Conference:** Proceedings of the 31st International Conference on Machine Learning (ICML-14)

**Year:** 2014

**Pages:** 10-18

**Abstract:** A mean function in reproducing kernel Hilbert space (RKHS), or a kernel mean, is an important part of many algorithms ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given a finite sample, an empirical average is the standard estimate for the true kernel mean. We show that this estimator can be improved due to a well-known phenomenon in statistics called Stein phenomenon. After consideration, our theoretical analysis reveals the existence of a wide class of estimators that are better than the standard one. Focusing on a subset of this class, we propose efficient shrinkage estimators for the kernel mean. Empirical evaluations on several applications clearly demonstrate that the proposed estimators outperform the standard kernel mean estimator.

[pdf] [BibTeX]

authors venues years

Suggest Changes to this paper.

Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).