Unbiased estimators are introduced for averaged Bregman divergences which generalize Stein's Unbiased (Predictive) Risk Estimator, and the minimization of these estimators is proposed as a regularization parameter selection method for regularization of inverse problems. Numerical experiments are presented in order to show the performance of the proposed technique. Experimental results indicate a useful occurence of a concentration of measure phenomena and some implications of this hypothesis are analyzed

翻译：对平均的布雷格曼差异引入了非偏见估计值,这些差异一般化了斯坦因的不偏见(预测性)风险估计器,并提议将这些估计器最小化作为使反向问题正规化的正规化参数选择方法。提出了数字实验,以显示拟议技术的性能。实验结果显示测量现象集中的有用发生,并分析了这一假设的某些影响。