Results 151 to 160 of about 935 (185)

The Quarternion Maximum Correntropy Algorithm

IEEE Transactions on Circuits and Systems II: Express Briefs, 2015
We 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 K. Paul
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Kernel recursive maximum correntropy with variable center

Signal Processing, 2022
Abstract In signal processing and machine learning, the maximum correntropy criterion with variable center (MCC-VC) has attracted more and more attention due to its robustness to non-zero mean noise. In this letter, we introduce MCC-VC into kernel space and develop the kernel recursive maximum correntropy with variable center (KRMCVC) algorithm.
Xiang Liu, Chengtian Song, Zhihua Pang
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A Separable Maximum Correntropy Adaptive Algorithm

IEEE Transactions on Circuits and Systems II: Express Briefs, 2020
In this brief, a separable maximum correntropy criterion (SMCC) algorithm is developed by exploiting the typical separability property of tensors. Utilizing the separability property, a great number savings are obtained along with accelerated learning rate and improved estimate accuracy. In the proposed SMCC, a correntropy scheme is used to construct a
Wanlu Shi, Yingsong Li 0001, Badong Chen
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Projected Kernel Recursive Maximum Correntropy

IEEE Transactions on Circuits and Systems II: Express Briefs, 2018
In 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 0005, Hongbin Zhang 0002, Gang Wang 0020   +2 more
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State space maximum correntropy filter

Signal Processing, 2017
The 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 0006, Hua Qu, Ji-hong Zhao 0001, Badong Chen   +3 more
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Robust constrained maximum total correntropy algorithm

Signal Processing, 2021
Abstract 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, Fuliang He, Shiyuan Wang, Herbert H. C. Iu   +3 more
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Maximum Total Complex Correntropy for Adaptive Filter

IEEE Transactions on Signal Processing, 2020
Nowadays, complex Correntropy has been widely used for adaptive filtering in the complex domain. Compared with the second order statistics methods, the complex correntropy based algorithms have shown the superiority in the non-Gaussian noise, especially the impulsive noise.
Guobing Qian, Shiyuan Wang, Herbert H. C. Iu   +2 more
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A robust maximum correntropy criterion for dictionary learning

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016
We introduce a method that incorporates robustness to one of the main building blocks of sparse modeling: dictionary learning. Particularly, we exploit correntropy to compute the principal components in cases where outliers might be detrimental without proper care.
Carlos A. Loza, José C. Príncipe
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Sequential Maximum Correntropy Kalman Filtering

Asian Journal of Control, 2018
AbstractThis paper explores a linear state estimation problem in non‐Gaussian setting and suggests a computationally simple estimator based on the maximum correntropy criterion Kalman filter (MCC‐KF). The first MCC‐KF method was developed in Joseph stabilized form. It requires two n × n and one m × m matrix inversions, where n is a dimension of unknown
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Robust PCA Through Maximum Correntropy Power Iterations

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Principal component analysis (PCA) is considered a quintessential data analysis technique when it comes to describing linear relationships between the features of a dataset. However, the well-known lack of robustness of PCA for non-Gaussian data and/or outliers often makes its practical use unreliable.
Jean P. Chereau, Bruno Scalzo, Danilo P. Mandic   +2 more
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