Adaptive Learning Algorithm for Principal Component Analysis With Partial Data

In this paper a fast and ecient adaptive learning algorithm for estimation of the principal components is developed. It seems to be especially useful in applications with changing environment , where the learning process has to be repeated in on{line manner. The approach can be called the cascade recursive least square (CRLS) method, as it combines a cascade (hierarchical) neural network scheme for input signal reduction with the RLS (recursive least square) lter for adaptation of learning rates. Successful application of the CRLS method for 2{D image compression{reconstruction and its performance in comparison to other known PCA adaptive algorithms are also documented.