An adaptive approach for optimal data reduction using recursive least squares learning method

An approach is introduced for the recursive computation of the principal components of a vector stochastic process. The neurons of a single-layer perceptron are sequentially trained using a recursive least squares (RLS)-type algorithm to extract the principal components of the input process. The proof of the convergence of the weights at the n th neuron to the nth principal component, given that the previous (n-1) training steps have determined the first (n -1) principal components, is established. Simulation results are given to show the accuracy and speed of this algorithm in comparison with previous methods