Using recursive least square learning method for principal and minor components analysis
A.S.Y. Wong, K.W. Wong, Chi-Sing Leung · 2002
In combining principal and minor components analysis, a parallel extraction method based on the recursive least square algorithm is suggested to extract the principal components of the input vectors. After the extraction, 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, which in turn indicate that our method effectively combines the extraction of the principal components and the pruning of the minor components.