A robust correntropy based subspace tracking algorithm in impulsive noise environments

Zhang, Jinfeng; Qiu, Tianshuang

doi:10.1016/j.dsp.2016.11.011

Cited by 18 publications

(11 citation statements)

References 20 publications

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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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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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show abstract

“…Correntropy criterion is a generalized correlation function of 2 random variables, which measures the similarity between them. [19][20][21][22][23][24][25][26][27][28][29][30][31] For 2 random variable d k (i) and y k (i), we can define the correntropy correlation function as follows:…”

Section: Single-task Global Optimizationmentioning

confidence: 99%

“…In our previous works, 17,18 the multitask learning over adaptive networks is derived based on maximum correntropy criterion (MCC). [19][20][21][22][23][24][25][26][27][28][29][30][31] The proposed learning algorithm determines a novel cooperation policy for sensors, in which they can collaborate with neighbors that have similar task and they can reject the collaboration of neighbors that have distinct tasks. In this paper, we want to analyze the transient performance of our proposed learning algorithm 17,18 and compare the theoretical results with simulation results.…”

mentioning

confidence: 99%

Transient performance analysis of adaptive multitask network based on correntropy criterion

Hajiabadi

Khoshbin

Hodtani

2017

Adaptive Control & Signal

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In this paper, we investigate the transient performance of the proposed distributed multitask learning algorithm that is developed based on maximum correntropy criterion. In the first stage, we derive the proposed multitask learning algorithm in which the correntropy-based combination matrix determines which sensors should collaborate together and which sensors should stop the collaboration. In the second stage, according to the variance relation of the error vector, we derive a closed-form relation that shows the transition of mean-square-deviation learning performance. We also find the lower and upper bounds of the step size that ensure the stability of the multitask learning algorithm. The theoretical finding of the transient performance is shown to fit a well match with simulation results. KEYWORDS adaptive network, correntropy criterion, distributed signal processing, multitask learning, transient analysis Int J Adapt Control Signal Process. 2018;32:229-247.wileyonlinelibrary.com/journal/acs

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“…erefore, alpha-stable distribution is usually used to de ne impulsive noise [12]. e conventional covariance matrix is calculated from the second-order statistics of the signal, which may be in nite when the data are corrupted by the extremely impulsive noise [13,14]. In addition, the conventional DOA algorithms cannot be decomposed into the signal subspace and the noise subspace with covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Median-Difference Correntropy for DOA under the Impulsive Noise Environment

Zhang

2019

Wireless Communications and Mobile Computing

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The source localization using direction of arrival (DOA) of target is an important research in the field of Internet of Things (IoTs). However, correntropy suffers the performance degradation for direction of arrival when the two signals contain the similar impulsive noise, which cannot be detected by the difference between two signals. This paper proposes a new correntropy, called the median-difference correntropy, which combines the generalized correntropy and the median difference. The median difference is defined as the deviation between the sampling value and the median of the signal, and it intuitively reflects the abnormality of impulsive noise. Then, the median difference is combined with the generalized correntropy to form a new weighting factor that can effectively suppress the amplitude level of impulsive noise. To improve the robustness of the algorithm, an adaptive kernel size is also integrated into the weighting factor to obtain the optimal local feature. The influence of adaptive kernel sizes on the proposed algorithm is simulated, and the comparison between three typical direction-of-arrival estimation algorithms is conducted. The results show that the accuracy of the median-difference correntropy is significantly superior to the correntropy-based correlation and the phased fractional lower-order moment for a wide range of alpha-stable distribution noise environments.

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A robust correntropy based subspace tracking algorithm in impulsive noise environments

Cited by 18 publications

References 20 publications

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Transient performance analysis of adaptive multitask network based on correntropy criterion

Median-Difference Correntropy for DOA under the Impulsive Noise Environment

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