论文标题
C-SUR：收缩估计器和原型分类器，用于复杂价值深度学习
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning
论文作者
论文摘要
James-Stein（JS）收缩估计器是一个有偏见的估计器，它捕获了高斯随机向量的平均值。尽管它在最大似然估计器（MLE）上具有理想的优势统计特性（MLE），而在均方误差（MSE）方面并没有太大进展，在将估算器扩展到流面评估估算值的数据方面并没有太大进展。 我们提出了C-SURE，这是一种新颖的Stein对复杂数据估计器的无偏风险估计值（确定），理论上证明了比MLE的最佳效果。为了调整复杂价值超现实分类器的体系结构，我们进一步将C-sure纳入了原型卷积神经网络（CNN）分类器中。我们将C-Sure与复杂值MSTAR和RADIOML数据集上的超现实和实价基线进行了比较。 C-SUR比超现实更准确，更健壮，对于同一原型分类器而言，收缩估计器总是比MLE更好。像超现实的C-Sure较小得多，在MSTAR（Radioml）上的实现基线的表现不到基线大小的1％（3％）
The James-Stein (JS) shrinkage estimator is a biased estimator that captures the mean of Gaussian random vectors.While it has a desirable statistical property of dominance over the maximum likelihood estimator (MLE) in terms of mean squared error (MSE), not much progress has been made on extending the estimator onto manifold-valued data. We propose C-SURE, a novel Stein's unbiased risk estimate (SURE) of the JS estimator on the manifold of complex-valued data with a theoretically proven optimum over MLE. Adapting the architecture of the complex-valued SurReal classifier, we further incorporate C-SURE into a prototype convolutional neural network (CNN) classifier. We compare C-SURE with SurReal and a real-valued baseline on complex-valued MSTAR and RadioML datasets. C-SURE is more accurate and robust than SurReal, and the shrinkage estimator is always better than MLE for the same prototype classifier. Like SurReal, C-SURE is much smaller, outperforming the real-valued baseline on MSTAR (RadioML) with less than 1 percent (3 percent) of the baseline size
