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Maximum correntropy criterion partial least squares
Optik, 2018Abstract Partial least squares (PLS) has been extensively used to solve problems such as infrared quantitative analysis, economic data analysis, object tracking. PLS finds a linear regression model by projecting the predicted variables and the response to a new space.
Yi Mou +4 more
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International Journal of Robust and Nonlinear Control
The traditional federated Kalman filter‐based multi‐source data fusion algorithm performs poorly in the presence of outliers and unknown noise, a modified federated robust Student's t maximum correntropy criterion variational adaptive Kalman filter is ...
Yunsheng Fan +3 more
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The traditional federated Kalman filter‐based multi‐source data fusion algorithm performs poorly in the presence of outliers and unknown noise, a modified federated robust Student's t maximum correntropy criterion variational adaptive Kalman filter is ...
Yunsheng Fan +3 more
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Mean-Variance Minimization Regularized Extreme Learning Machine With Maximum Correntropy Criterion
Journal of Computing and Information Science in EngineeringExtreme Learning Machine (ELM) has attracted significant concern in recent years due to its good generalization performance and fast running speed. However, it is very sensitive to modeling data in presence of non-Gaussian noise and outliers, resulting
Shufan Lin, Haiwei Fu, Kuaini Wang
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Nyström Kernel Algorithm Under Generalized Maximum Correntropy Criterion
IEEE Signal Processing Letters, 2020The kernel adaptive filters (KAFs) based on the minimum mean square error (MMSE) criterion in reproducing kernel Hilbert space (RKHS) improve the performance of linear adaptive filters but result in instability issues and large burdens of computation and memory in impulsive noises.
Tao Zhang 0183, Shiyuan Wang
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Volume 6: 19th International Conference on Micro- and Nanosystems (MNS); 21st International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC); 37th Conference on Mechanical Vibration and Sound (VIB); 38th Fluid Power and Motion Control Symposium (FPMC)
The linear parameter varying autoregressive moving average (LPV-ARMA) model is a powerful tool for analyzing non-stationary time series. However, conventional LPV-ARMA models typically rely on mean square error (MSE) minimization for parameter ...
Zihan Li, Yuejian Chen
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The linear parameter varying autoregressive moving average (LPV-ARMA) model is a powerful tool for analyzing non-stationary time series. However, conventional LPV-ARMA models typically rely on mean square error (MSE) minimization for parameter ...
Zihan Li, Yuejian Chen
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IEEE Transactions on Vehicular Technology
Accurate acquisition of critical vehicle states is a prerequisite for active safety systems to work properly. However, vehicle sates under non-ideal conditions are usually difficult to be measured directly due to the high cost of sensors.
Shuo Bai +5 more
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Accurate acquisition of critical vehicle states is a prerequisite for active safety systems to work properly. However, vehicle sates under non-ideal conditions are usually difficult to be measured directly due to the high cost of sensors.
Shuo Bai +5 more
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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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Transactions of the Institute of Measurement and Control
For enhancing the robustness of the Kalman filter in the presence of non-Gaussian noise or measurement outliers within a nonlinear state-space model, a robust filter based on mixed correlation entropy is proposed in this paper.
Dah-Jing Jwo +3 more
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For enhancing the robustness of the Kalman filter in the presence of non-Gaussian noise or measurement outliers within a nonlinear state-space model, a robust filter based on mixed correlation entropy is proposed in this paper.
Dah-Jing Jwo +3 more
semanticscholar +1 more source