Self-organizing algorithm of robust PCA based on single-layer NN

A way of improving the robustness of principal component analysis (PCA) is studied in order to increase the accuracy of feature extraction in bank check recognition. The two typical aspects of analyzing the robustness of the PCA algorithm are proposed and compared: one is based on the independence among the acquired principal components and the other is based on reducing the effects of the outliers in the training sample set. A new self-organizing algorithm of robust PCA is presented based on the structure of a single-layer neural network (NN) with modification of the cost function which stands for the reconstruction error of the input signal. The new nonlinear robust PCA algorithm can recognize outliers in the training sample set automatically and eliminate their effects on the accuracy and convergence of the PCA algorithm through proper processing of the recognized outliers.