Results 21 to 30 of about 708 (167)
Correntropy: A Localized Similarity Measure [PDF]
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006 The measure of similarity normally utilized in statistical signal processing is based on second order moments. In this paper, we reveal the probabilistic meaning of correntropy as a new localized similarity measure based on information theoretic learning (ITL) and kernel methods. As such it has vastly different properties when compared with mean square
Weifeng Liu 0016 +2 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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The Fast Correntropy Mace Filter [PDF]
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007 In this paper, we implement the newly introduced correntropy MACE filter using the fast Gauss transform (FGT). The correntropy MACE filter is a nonlinear extension to the MACE filter using the correntropy function in a feature space nonlinearly related to the input. The correntropy MACE outperforms the traditional linear MACE in both generalization and
Kyu-Hwa Jeong +2 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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Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method
Applied Sciences, 2022 In recent years, as a simple and effective method of noise reduction, singular value decomposition (SVD) has been widely concerned and applied. The idea of SVD for denoising is mainly to remove singular components (SCs) with small singular value (SV ...
Siqi Gong +5 more
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Kernel classifier with Correntropy loss [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 Classification can be seen as a mapping problem where some function of x n predicts the expectation of a class variable y n . This paper uses kernel methods for the prediction of class variable, together with a recently proposed cost function for classification, called Correntropy-loss (C-loss) function. C-Loss is a non-convex loss function based on a
Pokharel, Rosha, Principe, Jose C.
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Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction
Entropy, 2019 In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements.
Wenjuan Mei +4 more
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Correntropy based Granger causality [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 We propose a novel nonlinear extension to Granger causality. It is derived from a nonlinear mapping of a stochastic process using the recently introduced generalized correlation measure called correntropy. The method is demonstrated by detecting the direction of coupling in a chaotic system where the original Granger causality failed.
Il Park 0002, José Carlos Príncipe
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Adaptive identification under the maximum correntropy criterion with variable center
Радіоелектронні і комп'ютерні системи, 2022 The problem of identifying the parameters of a linear object in the presence of non-Gaussian noise is considered. The identification algorithm is a gradient procedure for maximizing the functional, which is a correntropy. This functionality allows you to
Oleg Rudenko, Oleksandr Bezsonov
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Regularized maximum correntropy machine [PDF]
Neurocomputing, 2015 In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples.
Jim Jing-Yan Wang +3 more
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