稳健在线回归的截尾最小误差熵
Trimmed Minimum Error Entropy for Robust Online Regression
摘要：
本文研究了非高斯噪声（特别是重尾噪声）环境下的在线线性回归问题。在这样的环境中，系统输出和标签之间的误差也不遵循高斯分布，并且可能存在误导学习过程的异常大的误差样本（或离群值）。主要的挑战是如何保持监督学习问题最少受到这些不需要的和误导性的离群值的影响。近年来，一种基于Renyi熵的信息论算法（称为最小误差熵）被用来解决这个问题。然而，这种最小化可能不会导致期望的估计器，因为熵是平移不变的，即，通过使误差熵最小化，误差样本可以不必集中在零附近。在本文中，提出了一种量化技术，通过该技术，不仅解决了上述需要的设置误差在原点周围，但也主要的离群点被拒绝从基于MEE的学习和收敛速度，稳态失调，并测试误差的观点，提高了可重构性。
英文摘要：
In this paper, online linear regression in environments corrupted by non-Gaussian noise (especially heavy-tailed noise) is addressed. In such environments, the error between the system output and the label also does not follow a Gaussian distribution and there might exist abnormally large error samples (or outliers) which mislead the learning process. The main challenge is how to keep the supervised learning problem least affected by these unwanted and misleading outliers. In recent years, an information theoretic algorithm based on Renyi's entropy, called minimum error entropy (MEE), has been employed to take on this issue. However, this minimization might not result in a desired estimator inasmuch as entropy is shift-invariant, i.e., by minimizing the error entropy, error samples may not be necessarily concentrated around zero. In this paper, a quantization technique is proposed by which not only aforementioned need of setting errors around the origin in MEE is addressed, but also major outliers are rejected from MEE-based learning and MEE performance is improved from convergence rate, steady state misalignment, and testing error points of view.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
