Results 151 to 160 of about 3,111 (174)
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Correntropy-Induced Robust Low-Rank Hypergraph
IEEE Transactions on Image Processing, 2019Hypergraph learning has been widely exploited in various image processing applications, due to its advantages in modeling the high-order information. Its efficacy highly depends on building an informative hypergraph structure to accurately and robustly formulate the underlying data correlation.
Taisong Jin +5 more
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Sequential Maximum Correntropy Kalman Filtering
Asian Journal of Control, 2018AbstractThis 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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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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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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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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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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Correntropy and Linear Representation
2014The nearest neighbor (NN) classifier is the most popular method for image-based object recognition. In NN classifier, the representational capacity of an image database and the recognition rate depend on how registered samples are selected to represent object’s possible variations and also how many samples are available.
Ran He +3 more
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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
Badong Chen +5 more
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Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l(1)norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy ...
Ran, He, Wei-Shi, Zheng, Bao-Gang, Hu
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