ADAPTIVE ROBUST PRINCIPAL COMPONENT ANALYSIS BASED ON ERROR MODELING
基于误差模型的自适应鲁棒主成分分析

One way to improve the robustness of principal component analysis (PCA) is studied in the paper. A new adaptive algorithm of robust PCA based on the structure of single layer neural network (NN) is developed with modification of the cost function which can be acquired through modeling of the error function. The new nonlinear robust PCA algorithm can reduce the effects of outliers on the accuracy and convergence of the PCA algorithm through proper processing of them.