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Multi-Kernel Maximum Correntropy Kalman Filter
IEEE Control Systems Letters, 2022Maximum correntropy criterion (MCC) has been widely used in Kalman filter to cope with heavy-tailed measurement noises. However, its performance on mitigating non-Gaussian process noises and unknown disturbance is rarely explored. In this letter, we extend the definition of correntropy from a single kernel to multiple kernels.
Shilei Li +3 more
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Robust constrained maximum total correntropy algorithm
Signal Processing, 2021Abstract Constrained adaptive filtering has been paid more attentions recently. As a robust constrained adaptive filtering algorithm, constrained maximum correntropy criterion (CMCC) has shown its superiority for the output data contaminated by heavy-tail impulsive noises.
Guobing Qian +3 more
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Kernel recursive maximum correntropy
Signal Processing, 2015In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum correntropy (KRMC), is derived in kernel space and under the maximum correntropy criterion (MCC). The proposed algorithm is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive ...
Zongze Wu +4 more
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Robust Multikernel Maximum Correntropy Filters
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020The multikernel adaptive filters based on the minimum mean square error (MMSE) criterion have been proposed to improve the performance of the kernel least mean square (KLMS), efficiently. However, these multikernel methods suffer from large computational burden as well as instability in impulsive noises.
Kui Xiong, Wei Shi, Shiyuan Wang
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The Quarternion Maximum Correntropy Algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs, 2015We develop a kernel adaptive filter for quaternion data based on maximizing correntropy. We apply a modified form of the HR calculus that is applicable to Hilbert spaces for evaluating the cost function gradient to develop the quaternion kernel maximum correntropy (KMC) algorithm. The KMC method uses correntropy to measure similarity between the filter
Tokunbo Ogunfunmi, Thomas Paul
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State space maximum correntropy filter
Signal Processing, 2017The state space recursive least squares (SSRLS) filter is a new addition to the well-known recursive least squares (RLS) family filters, which can achieve an excellent tracking performance by overcoming some limitations of the standard RLS algorithm.
Xi Liu, Hua Qu, Jihong Zhao, Badong Chen
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Kernel Recursive Generalized Maximum Correntropy
IEEE Signal Processing Letters, 2017In this letter, a novel kernel adaptive algorithm, called kernel recursive generalized maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the generalized maximum correntropy (GMC) criterion. The proposed kernel algorithm can effectively scale down the dynamic recursive weight coefficients influenced by the impulsive estimate ...
Ji Zhao, Hongbin Zhang
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Projected Kernel Recursive Maximum Correntropy
IEEE Transactions on Circuits and Systems II: Express Briefs, 2018In this brief, a different kernel recursive maximum correntropy algorithm is derived using the weighted output information, called KRMC-W. To curb the network growth, we propose a new online sparsification strategy in a feature space, named vector projection (VP) method.
Ji Zhao, Hongbin Zhang, Gang Wang
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Maximum Correntropy Criterion-Based Hierarchical One-Class Classification
IEEE Transactions on Neural Networks and Learning Systems, 2021Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and ...
Jiuwen Cao +5 more
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Robust M-estimation-based maximum correntropy Kalman filter
ISA Transactions, 2023In this paper, a framework that combines an M-estimation and information-theoretic-learning (ITL)-based Kalman filter under impulsive noises is presented. The ITL-based methods make the most of the features of the data itself and can improve robustness by choosing an appropriate kernel bandwidth. However, small kernel bandwidths may lead to divergence.
Chen Liu +3 more
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