Results 171 to 180 of about 1,526 (199)
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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.
Yidan Wang, 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, Jinxin Wang
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Correntropy-Based Multiview Subspace Clustering
IEEE Transactions on Cybernetics, 2021Multiview subspace clustering, which aims to cluster the given data points with information from multiple sources or features into their underlying subspaces, has a wide range of applications in the communities of data mining and pattern recognition. Compared with the single-view subspace clustering, it is challenging to efficiently learn the structure
Lei Xing 0003 +4 more
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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 0005 +2 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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An adaptive kernel width update for correntropy
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Correntropy, as an adaptive criterion of Information Theoretic Learning (ITL), has been successfully used in signal processing and machine learning. How to appropriately select the kernel width of correntropy is a crucial problem in correntropy applications. Existing kernel width selection methods are not suitable enough for this problem. In this paper,
Songlin Zhao +2 more
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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 0005, Hongbin Zhang 0002
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A Separable Maximum Correntropy Adaptive Algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020In 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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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 0006 +3 more
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