Convergence analysis of a deterministic discrete time system of feng's MCA learning algorithm

The convergence of minor-component analysis (MCA) algorithms is an important issue with bearing on the use of these methods in practical applications. This correspondence studies the convergence of Feng's MCA learning algorithm via a corresponding deterministic discrete-time (DDT) system. Some sufficient convergence conditions are obtained for Feng's MCA learning algorithm with constant learning rate. Simulations are carried out to illustrate the theory.