Results 1 to 10 of about 543 (157)
Maximum Correntropy Criterion with Distributed Method [PDF]
Mathematics, 2022 The Maximum Correntropy Criterion (MCC) has recently triggered enormous research activities in engineering and machine learning communities since it is robust when faced with heavy-tailed noise or outliers in practice.
Fan Xie +3 more
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Online Gradient Descent for Kernel-Based Maximum Correntropy Criterion [PDF]
Entropy, 2019 In the framework of statistical learning, we study the online gradient descent algorithm generated by the correntropy-induced losses in Reproducing kernel Hilbert spaces (RKHS).
Baobin Wang, Ting Hu
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Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes [PDF]
Sensors, 2018 In this paper, we investigate the state estimation of systems with unknown covariance non-Gaussian measurement noise. A novel improved Gaussian filter (GF) is proposed, where the maximum correntropy criterion (MCC) is used to suppress the pollution of ...
Guoqing Wang +3 more
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Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation [PDF]
Sensors, 2016 A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks.
Xi Liu +4 more
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Student’s t-Kernel-Based Maximum Correntropy Kalman Filter [PDF]
Sensors, 2022 The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the ...
Hongliang Huang, Hai Zhang
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Partial maximum correntropy regression for robust electrocorticography decoding [PDF]
Frontiers in Neuroscience, 2023 The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface.
Yuanhao Li +4 more
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Maximum Correntropy with Variable Center Unscented Kalman Filter for Robust Power System State Estimation [PDF]
Entropy, 2022 The robust Kalman filter with correntropy loss has received much attention in recent years for forecasting-aided state estimation in power systems, since it efficiently reduces the negative influence of various abnormal situations, such as non-Gaussian ...
Zhenglong Sun, Chuanlin Liu, Siyuan Peng
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Maximum correntropy unscented filter [PDF]
International Journal of Systems Science, 2017 The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function.
Paul Honeine, Xi Liu, Badong Chen
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Maximum correntropy Kalman filter [PDF]
Automatica, 2017 Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously.
JOSÉ Principe, Xi Liu, Haiquan Zhao
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Frequency domain maximum correntropy criterion spline adaptive filtering [PDF]
Scientific Reports, 2021 A filtering algorithm based on frequency domain spline type, frequency domain spline adaptive filters (FDSAF), effectively reducing the computational complexity of the filter.
Wenyan Guo, Yongfeng Zhi, Kai Feng
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