A Modified Oja-Xu MCA Learning Algorithm and Its Convergence Analysis

Dezhong Peng, Zhang Yi

Abstract: The original Oja-Xu minor component analysis (MCA) learning algorithm is not convergent. This brief shows that by modifying Oja-Xu MCA learning algorithm with a normalization step the modified one could be convergent subject to some conditions satisfied. The convergence of the modified MCA learning algorithm is studied by analyzing the convergence of an associated deterministic discrete time system. Necessary and sufficient conditions for convergence are obtained. Simulations further confirm the results