鲁棒核主元分析的数据重构

Data Reconstruction Based on Robust Kernel Principal Component Analysis

针对野值点噪声对样本均值和协方差估计带来的不利影响,在线性鲁棒M位置估计方法的基础上,结合了核原理来估计协方差,提出了一种新型的鲁棒KPCA(核主元分析)算法.将所提出的算法应用于数据重构仿真实验,仿真测试结果表明当样本数据中存在野值点噪声时,由所提出的鲁棒KPCA算法实现样本数据重构时,要比KPCA具有更高的重构精度,抗野值点噪声性能更强.

To avoid the adverse effects of outliers noise on the sample mean and covariance,a new robust kernel principal component analysis（R-KPCA） is presented with the covariance estimated by combining the linear robust M-estimation of location with the kernel function.The presented algorithm is employed in a data reconstruction,and the simulation results show that the de-noise performance of the presented robust KPCA is better than that of KPCA to process the outlier noise. Moreover,the presented algorithm for data reconstruction has a higher precision than KPCA.