The Nyström Kernel Conjugate Gradient Algorithm Based on $k$ -Means Sampling

Abstract

The kernel conjugate gradient (KCG) algorithms have been proposed to improve the convergence rate and the filtering accuracy of kernel adaptive filters (KAFs) efficiently. However, sparsification is necessary in the KCG algorithms to curb the growth of network structure for online applications. To this end, a novel online kernel conjugate gradient algorithm under the mean square error criterion is proposed to approximate the kernel matrix in KAFs by combining k-means sampling into the Nyström method in a fixed-dimensional feature space, namely a Nyström kernel conjugate gradient algorithm based on k-means sampling (NysKCG-KM). The approximation accuracy of the kernel matrix as well as the filtering performance of NysKCG-KM are therefore guaranteed by k-means sampling. The proposed NysKCG-KM with no requirement of sparsification can achieve dramatically better filtering accuracy than the kernel least mean square with sparsification, and approach the filtering accuracy of the kernel recursive least squares with sparsification. Monte Carlo simulations using both the synthetic and real-world data validate the superiorities of the proposed NysKCG-KM.

K-Means Algorithm to Identify the Elderly Psychological Stress Analysis Algorithm

“…First, the linear and nonlinear features extracted from the EEG signal were compared and then combined with the evaluation scale of PSQL and LZC complexity, alpha relative power, and other characteristics, and finally, the psychological stress state was more effectively evaluated; 36 groups of ECG signals, 18 groups of surface EMG signals, and 18 groups of finger pulse wave signals were collected from nine testers, using these signals as the raw data to identify psychological stress, and then, the DS evidence theory was combined with the SVM algorithm to build a stress recognition model. Achieving the recognition of psychological stress, it proves the effectiveness of this model in assessing psychological stress states [8]. Aiming at the individual differences in identifying psychological stress, the algorithm was improved and studied.…”