量化最小误差熵准则
Quantized Minimum Error Entropy Criterion
摘要：
与均方误差（MSE）等传统的学习准则相比，最小误差熵准则在非线性、非高斯信号处理和机器学习中具有优越性。Renyis熵估计中的对数幅角称为信息势（IP），是信息论学习（ITL）中的一个常用的代价。然而，由于双重求和，IP的计算复杂度在样本数量方面是二次的。这造成了计算瓶颈，尤其是对于大规模数据集。为了解决这个问题，在这项工作中，我们提出了一种有效的量化方法来减少IP的计算负担，这将复杂度从O（N*N）降低到O（MN），其中M << N。新的学习准则被称为量化学习准则（QRST）。本文给出了Qsq的一些基本性质。最后给出了几个实例，验证了该算法的优良性能.
英文摘要：
Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning. The argument of the logarithm in Renyis entropy estimator, called information potential (IP), is a popular MEE cost in information theoretic learning (ITL). The computational complexity of IP is however quadratic in terms of sample number due to double summation. This creates computational bottlenecks especially for large-scale datasets. To address this problem, in this work we propose an efficient quantization approach to reduce the computational burden of IP, which decreases the complexity from O(N*N) to O (MN) with M << N. The new learning criterion is called the quantized MEE (QMEE). Some basic properties of QMEE are presented. Illustrative examples are provided to verify the excellent performance of QMEE.
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