Combining PCA and MCA by using recursive least square learning method

By using the fact that the derivatives of the ith network output with respect to the weights connected to the jth output neuron (i≠j) are zero, a modified RLS method is proposed for principal and minor components analysis. After the extraction of significant components of the input vectors, the error covariance matrix obtained in the learning process is used to perform minor components analysis. The minor components found are then pruned so as to achieve a higher compression ratio. Simulation results show that both the convergent speed and the compression ratio are improved. These indicate that our method combines the extraction of principal components and the pruning of minor components effectively