Search Machine Learning Repository: @inproceedings{icml2014c1_muandet14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Kernel Mean Estimation and Stein Effect},
    Url = {http://jmlr.org/proceedings/papers/v32/muandet14.pdf},
    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.},
    Author = {Krikamol Muandet and Kenji Fukumizu and Bharath Sriperumbudur and Arthur Gretton and Bernhard Schölkopf},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {10-18}
   }