Constrained maximum correntropy adaptive filtering

Peng, Siyuan; Chen, Badong; Sun, Lei; Ser, Wee; Lin, Zhiping

doi:10.1016/j.sigpro.2017.05.009

Cited by 73 publications

(23 citation statements)

References 43 publications

(44 reference statements)

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“…The concept of information entropy is proposed by Shannon in 1948, who is regarded as the father of information theory. At present, the applications of entropy in biomedical signals have received extensive research attention [ 34 , 35 , 36 ]. For instance, Pincus [ 37 ] proposed the concept of approximate entropy to examine the changes of heart rate for infantile sudden illness.…”

Section: Methodsmentioning

confidence: 99%

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

Angsuwatanakul

O’Reilly

Ounjai

et al. 2020

Entropy

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The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will. In a continuous stimulus set, 250 indoor/outdoor scenes were presented at random, between periods of fixation on a black background. The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image; both stimulus and response times were stored for analysis. The participants then performed a scene recognition test to confirm whether or not they remembered the images. The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene; p < 0.05 (two-tailed). This suggests that simultaneous EEG and fNIRS can be used for brain functional analysis, and MSE might be the potential indicator for this multimodality approach.

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Section: Methodsmentioning

confidence: 99%

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

Angsuwatanakul

O’Reilly

Ounjai

et al. 2020

Entropy

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“…α NM = 1, β NM = 0.5, γ NM = 2, and σ NM = 0.5. (18) This is a nearly universal choice in the NMS method. It also reflects the fact that the efficacy optimization is a regular problem for the NMS method.…”

Section: ) Coefficientsmentioning

confidence: 99%

“…Besides, the ZMNL functions are simple and easy to implement in traditional structures. The other kind is adaptive filtering, which is usually designed under a carefully chosen criterion, such as the least square criterion [14], the minimum dispersion criterion [17], the maximum correntropy criterion [18], the logarithmic least mean pth-power criterion [19].…”

Section: Introductionmentioning

confidence: 99%

Nonlinearity Design With Power-Law Tails for Correlation Detection in Impulsive Noise

Luo

Jonckheere

2020

IEEE Access

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Impulsive noise plays an important role in power line communication among other applications. To improve the communication performance, this paper proposes a novel design of nonlinear processing which improves the fundamental performance of signal detection in impulsive noise. Power-law tails are firstly introduced in nonlinearity design to provide adjustable decay factors for different distributions. Four modes of nonlinearity functions are developed and analyzed. By taking the exponent and the threshold as two arguments, we formulate the nonlinearity design as an optimization problem of maximizing the efficacy function, which is the fundamental measurement for detecting a deterministic signal in impulsive noise. Given that the efficacy function is differentiable, unimodal but without closed-form derivatives, we propose to solve the optimization problem by derivative-free methods, e.g. the Nelder-Mead simplex method. As concept demonstration, our method is used for three commonly-used distribution examples. Results show that our nonlinearity design can achieve almost the same efficacy and detection performance as the locally optimal detector, with the advantage of easy-to-apply closed form expressions.

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“…The main property of correntropy is that it provides an effective mechanism to mitigate the influence of large outliers. Recently, correntropy has been successfully applied in various areas, such as signal processing [23], machine learning [24][25][26], adaptive filtering [27][28][29], and others [30][31][32].…”

Section: Mixture Correntropymentioning

confidence: 99%

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Xue

Luo

Gao

et al. 2019

Entropy

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Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low filtering accuracy. Hence, a kernel conjugate gradient (KCG) algorithm has been proposed with low computational complexity, while achieving comparable performance to some KAF algorithms, e.g., the kernel recursive least squares (KRLS). However, the robust learning performance is unsatisfactory, when using KCG. Meanwhile, correntropy as a local similarity measure defined in kernel space, can address large outliers in robust signal processing. On the basis of correntropy, the mixture correntropy is developed, which uses the mixture of two Gaussian functions as a kernel function to further improve the learning performance. Accordingly, this article proposes a novel KCG algorithm, named the kernel mixture correntropy conjugate gradient (KMCCG), with the help of the mixture correntropy criterion (MCC). The proposed algorithm has less computational complexity and can achieve better performance in non-Gaussian noise environments. To further control the growing radial basis function (RBF) network in this algorithm, we also use a simple sparsification criterion based on the angle between elements in the reproducing kernel Hilbert space (RKHS). The prediction simulation results on a synthetic chaotic time series and a real benchmark dataset show that the proposed algorithm can achieve better computational performance. In addition, the proposed algorithm is also successfully applied to the practical tasks of malware prediction in the field of malware analysis. The results demonstrate that our proposed algorithm not only has a short training time, but also can achieve high prediction accuracy.

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Constrained maximum correntropy adaptive filtering

Cited by 73 publications

References 43 publications

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

Nonlinearity Design With Power-Law Tails for Correlation Detection in Impulsive Noise

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

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