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Kernel Kalman Filtering With Conditional Embedding and Maximum Correntropy Criterion
IEEE Transactions on Circuits and Systems I: Regular Papers, 2019The Hilbert space embedding provides a powerful and flexible tool for dealing with the nonlinearity and high-order statistics of random variables in a dynamical system. The kernel Kalman filtering based on the conditional embedding operator (KKF-CEO) shows significant performance improvements over the traditional Kalman filters in the noisy nonlinear ...
Lujuan Dang +4 more
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Robust feature learning by stacked autoencoder with maximum correntropy criterion
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014Unsupervised feature learning with deep networks has been widely studied in the recent years. Despite the progress, most existing models would be fragile to non-Gaussian noises and outliers due to the criterion of mean square error (MSE). In this paper, we propose a robust stacked autoencoder (R-SAE) based on maximum correntropy criterion (MCC) to deal
Yu Qi +3 more
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A Norm Penalized Noise-free Maximum Correntropy Criterion Algorithm
2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019l 1 -norm penalty and noise-free approach are considered in this paper to contribute to a maximum correntropy criterion (MCC) based algorithm. The introduced $l$ 1 -norm constrained noise-free MCC (L 1 -NFMCC) algorithm inherits the good behavior of MCC in non-Gaussian environments.
Wanlu Shi, Yingsong Li 0001, Felix Albu
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Generalization analysis of deep CNNs under maximum correntropy criterion
Neural NetworksConvolutional neural networks (CNNs) have gained immense popularity in recent years, finding their utility in diverse fields such as image recognition, natural language processing, and bio-informatics. Despite the remarkable progress made in deep learning theory, most studies on CNNs, especially in regression tasks, tend to heavily rely on the least ...
Yingqiao Zhang, Zhiying Fang, Jun Fan
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Robust locality preserving projection based on maximum correntropy criterion
Journal of Visual Communication and Image Representation, 2014LPP-MCC utilizes the maximum correntropy as the distance metric.The objective problem of LPP-MCC is solved via a half-quadratic optimization procedure.LPP-MCC is more robust to large outliers than LPP-L2 and LPP-L1.LPP-MCC avoids the small sample size (SSS) problem.
Fujin Zhong, Defang Li, Jiashu Zhang
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A New Approach To Online Regression Based On Maximum Correntropy Criterion
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), 2019The problem of linear adaptive filtering (or equivalently, online regression) in the presence of non-Gaussian noise is addressed. One efficient way in face of environments with non-Gaussian noise is to employ information theoretic criteria such as correntropy.
Sajjad Bahrami, Ertem Tuncel
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An Efficient Parameter Optimization of Maximum Correntropy Criterion
IEEE Signal Processing Letters, 2023Long Shi 0002, Lu Shen, Badong Chen
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Adaptive time delay estimation based on the maximum correntropy criterion
Digital Signal Processing, 2019Abstract In this paper, a novel adaptive time delay estimation (TDE) method under the existence of amplitude attenuation is proposed for the impulsive noise environment. We present a closed form of the recursive solution for the TDE by using the maximum correntropy criterion (MCC).
Fangxiao Jin, Tianshuang Qiu
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Kernel Least Mean Square With Maximum Correntropy Criterion
2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS), 2022Yawen Li 0001 +3 more
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Adaptive Convex Combination of Kernel Maximum Correntropy Criterion
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), 2022Long Shi, Yunchen Yang
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