Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction
AbstractKernel 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. View Full-Text
Share & Cite This Article
Xue, N.; Luo, X.; Gao, Y.; Wang, W.; Wang, L.; Huang, C.; Zhao, W. Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction. Entropy 2019, 21, 785.
Xue N, Luo X, Gao Y, Wang W, Wang L, Huang C, Zhao W. Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction. Entropy. 2019; 21(8):785.Chicago/Turabian Style
Xue, Nan; Luo, Xiong; Gao, Yang; Wang, Weiping; Wang, Long; Huang, Chao; Zhao, Wenbing. 2019. "Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction." Entropy 21, no. 8: 785.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.