Adaptive distributed orthogonalization processing for principal components analysis
His-Jen Chen, R.-W. Liu · 1992
Adaptive extraction of principal components of a vector stochastic process is a topic currently receiving much attention. The authors propose a learning algorithm implemented on a neural-like network. This algorithm is shown to be superior to previous ones. The convergence of this algorithm can be proved, but only an outline of the proof is presented.>