College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China.
Neural Netw. 2023 Nov;168:405-418. doi: 10.1016/j.neunet.2023.09.034. Epub 2023 Sep 22.
Minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data.
最小误差摘（MEEF）受到了广泛关注，因为它在机器学习和信号处理领域具有出色的性能，可以抑制非高斯噪声的负面影响。然而，MEEF 的信息势估计涉及到一个基于所有可用误差样本的双重求和运算符，这在许多实际场景中会导致巨大的计算负担。因此，本文采用了一种有效的量化方法，用一个较小的子集来表示主要的误差样本集，生成量化最小误差摘（QMEEF）。从理论角度介绍并证明了 QMEEF 的一些基本性质。此外，我们还将该新准则应用于训练一类参数线性模型，包括常用的线性回归模型、随机向量函数链接网络和广义学习系统作为特例。在各种数据集上的实验结果表明，该方法在处理污染数据的回归任务时具有良好的性能。
