Results 11 to 20 of about 3,111 (174)
Cyclic Correntropy: Foundations and Theories
IEEE Access, 2018 Over the past several decades, cyclostationarity has been regarded as one of the most significant theories in the research of non-stationary signal processing; therefore, it has been widely used to solve a large variety of scientific problems, such as ...
Tao Liu, Tianshuang Qiu, Shengyang Luan
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Multikernel Correntropy for Robust Learning [PDF]
IEEE Transactions on Cybernetics, 2022 As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel.
Badong Chen +5 more
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Overexpression of miR-516a-5p Promotes Erosive Oral Lichen Planus: In Vitro Study Based on Bioinformatics Analyses. [PDF]
Clin Exp Dent ResABSTRACT Objectives This study aimed to investigate the differentially expressed microRNAs in erosive oral lichen planus, followed by analyzing how the overexpression of identified miR‐516a‐5p influences human oral mucosal fibroblasts. Material and Methods High‐throughput sequencing using tissues from patients and healthy individuals identified varying
Chen Y +5 more
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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.
Liu, Xi +4 more
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Correntropy Based Matrix Completion [PDF]
Entropy, 2018 This paper studies the matrix completion problems when the entries are contaminated by non-Gaussian noise or outliers. The proposed approach employs a nonconvex loss function induced by the maximum correntropy criterion. With the help of this loss function, we develop a rank constrained, as well as a nuclear norm regularized model, which is resistant ...
Yang, Yuning +2 more
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Fractional‐order complex correntropy algorithm for adaptive filtering in α‐stable environment
Electronics Letters, 2021 In adaptive filtering applications, the Gaussian distribution cannot be used to model the signal/noise with frequent spikes accurately. In fact, the rational model to simulate the behaviour of such signal/noise is the α‐stable distribution process.
Chen Qiu +3 more
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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.
Chen, Badong +3 more
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Robust large-scale clustering based on correntropy. [PDF]
PLoS One, 2022 With the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs to be solved urgently. Especially in the face of large-scale real-world data, which contains a large number of complex distributions of noises and outliers, the research on robust large-scale real-world data clustering ...
Jin G, Gao J, Tan L.
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Constrained maximum correntropy adaptive filtering [PDF]
Signal Processing, 2017 Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under mean square error (MSE) criterion, which is an ideal ...
Peng, Siyuan +4 more
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Remote Sensing, 2022 To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the ...
Tianrui Liao +4 more
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