Results 141 to 150 of about 2,111 (181)

A Separable Maximum Correntropy Adaptive Algorithm

IEEE Transactions on Circuits and Systems II: Express Briefs, 2020
In 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, Badong Chen
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Maximum Correntropy Criterion for Robust Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
In 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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Kernel recursive maximum correntropy with variable center

Signal Processing, 2022
Abstract In signal processing and machine learning, the maximum correntropy criterion with variable center (MCC-VC) has attracted more and more attention due to its robustness to non-zero mean noise. In this letter, we introduce MCC-VC into kernel space and develop the kernel recursive maximum correntropy with variable center (KRMCVC) algorithm.
Xiang Liu, Chengtian Song, Zhihua Pang
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Sequential Maximum Correntropy Kalman Filtering

Asian Journal of Control, 2018
AbstractThis 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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Maximum correntropy criterion partial least squares

Optik, 2018
Abstract 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, Long Zhou, Weizhen Chen, Jijun Fan, Xu Zhao   +4 more
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A distributed maximum correntropy Kalman filter

Signal Processing, 2019
Abstract 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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Maximum Correntropy Criterion Constrained Kalman Filter

Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications, 2017
Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in ...
Seyed Fakoorian, Mahmoud Moosavi, Reza Izanloo, Vahid Azimi, Dan Simon   +4 more
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Convex regularized recursive maximum correntropy algorithm

Signal Processing, 2016
In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced ...
Xie Zhang, Kaixin Li, Zongze Wu, Yuli Fu, Haiquan Zhao, Badong Chen   +5 more
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Robust PCA Through Maximum Correntropy Power Iterations

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Principal component analysis (PCA) is considered a quintessential data analysis technique when it comes to describing linear relationships between the features of a dataset. However, the well-known lack of robustness of PCA for non-Gaussian data and/or outliers often makes its practical use unreliable.
Jean P. Chereau, Bruno Scalzo, Danilo P. Mandic   +2 more
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Generalized maximum correntropy detector for non‐Gaussian environments

International Journal of Adaptive Control and Signal Processing, 2017
SummaryThis paper addresses the problem of multiple‐hypothesis detection. In many applications, assuming the Gaussian distribution for undesirable disturbances does not yield a sufficient model. On the other hand, under the non‐Gaussian noise/interference assumption, the optimal detector will be impractically complex. Therewith, inspired by the optimal
Saeed Hakimi, Ghosheh Abed Hodtani
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