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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)
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.
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