A unified sequential method for PCA
A.S.Y. Wong, K.W. Wong, Chi-Sing Leung · 2003
We propose a strictly local unified sequential method for principal component analysis. Any principal component analysis algorithm for linear feedforward neural networks can be used as the weight updating equation in our method. When principal components are extracted one by one sequentially, we suggest that the initial weight vector for the next component extraction should be orthogonal to the eigen-subspace already extracted. Simulation results show that both the convergence and the precision of the extraction are improved. Our method is also capable of extracting full eigenspace by using the neural network approach.