C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning

• URL: http://arxiv.org/abs/2006.12590v1
• Date: Mon, 22 Jun 2020 20:02:20 GMT
• Title: C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning
• Authors: Yifei Xing, Rudrasis Chakraborty, Minxuan Duan, Stella Yu
• Abstract summary: We propose C-SURE, a novel Stein's unbiased risk estimate (SURE) of the JS estimator on the manifold of complex-valued data. C-SURE is more accurate and robust than SurReal, and the shrinkage estimator is always better than MLE for the same prototype classifier.
• Score: 15.906530504220179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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