Results 131 to 140 of about 708 (167)
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Mixture correntropy for robust learning
Pattern Recognition, 2018Abstract Correntropy is a local similarity measure defined in kernel space, hence can combat large outliers in robust signal processing and machine learning. So far, many robust learning algorithms have been developed under the maximum correntropy criterion (MCC), among which, a Gaussian kernel is generally used in correntropy. To further improve the
Jing Qin, Shiyuan Wang, Jiuwen Cao
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Generalized Correntropy for RobustAdaptive Filtering [PDF]
IEEE Transactions on Signal Processing, 2016 As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel,
JOSÉ Principe +2 more
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Maximum Correntropy Criterion With Variable Center [PDF]
IEEE Signal Processing Letters, 2019 5 pages, 1 ...
Yingsong Li, JOSÉ Principe, Xin Wang
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Cyclostationary correntropy: Definition and applications
Expert Systems With Applications, 2017Abstract Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and ...
Aluisio Fontes, Luiz F Q Silveira
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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 K. Paul
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Robust 2DLDA based on correntropy
Neurocomputing, 2018Abstract To further improve the robustness of two-dimensional LDA (2DLDA) methods against outliers, this paper proposes a new robust 2DLDA version which obtains the optimal projection transformation by maximizing the correntropy-based within-class similarity and maintaining the global dispersity simultaneously.
Fujin Zhong, Li Liu 0030, Jun Hu 0002
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A robust classification framework with mixture correntropy
Information Sciences, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liming Yang
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A distributed maximum correntropy Kalman filter
Signal Processing, 2019Abstract Most distributed Kalman filters are based on the cost function of the well-known minimum mean square estimation criterion, which performs well in the presence of Gaussian noise. When impulsive noise is involved, the performance of distributed Kalman filters may become worse.
Gang Wang, Rui Xue
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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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Correntropy with Nonnegative Constraint
2014Nonnegativity constraint is more consistent with the biological modeling of visual data and often leads to better performance for data representation and graph learning [66]. In this chapter, we present an overview of some recent advances in correntropy with nonnegative constraint.
Ran He +3 more
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