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ADMM for maximum correntropy criterion [PDF]
2016 International Joint Conference on Neural Networks (IJCNN), 2016 The correntropy provides a robust criterion for outlier-insensitive machine learning, and its maximisation has been increasingly investigated in signal and image processing. In this paper, we investigate the problem of unmixing hyperspectral images, namely decomposing each pixel/spectrum of a given image as a linear combination of other pixels/spectra ...
Zhu, Fei +4 more
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Entropy, 2020 The complex correntropy has been successfully applied to complex domain adaptive filtering, and the corresponding maximum complex correntropy criterion (MCCC) algorithm has been proved to be robust to non-Gaussian noises.
Fei Dong, Guobing Qian, Shiyuan Wang
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Correntropy based Robust Decomposition of Neuromodulations [PDF]
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 Neuromodulations as observed in the extracellular electrical potential recordings obtained from Electroencephalograms (EEG) manifest as organized, transient patterns that differ statistically from their featureless noisy background. Leveraging on this statistical dissimilarity, we propose a noniterative robust classification algorithm to isolate, in ...
Akella, Shailaja, Principe, Jose C.
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Frontiers in Energy Research, 2022 The classical Kalman filter is a very important state estimation approach, which has been widely used in many engineering applications. The Kalman filter is optimal for linear dynamic systems with independent Gaussian noises.
Guanghua Zhang +5 more
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Local Matrix Feature-Based Kernel Joint Sparse Representation for Hyperspectral Image Classification
Remote Sensing, 2022 Hyperspectral image (HSI) classification is one of the hot research topics in the field of remote sensing. The performance of HSI classification greatly depends on the effectiveness of feature learning or feature design. Traditional vector-based spectral–
Xiang Chen +3 more
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Generalized Correntropy for RobustAdaptive Filtering [PDF]
IEEE Transactions on Signal Processing, 2016 As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel,
Badong Chen +4 more
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A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding
Entropy, 2021 Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients.
Xi Liu +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, Jose C. Principe
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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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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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