Data Reconstruction Based on Robust Kernel Principal Component Analysis

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.