Convergence Analysis of an Effective MCA Learning Algorithm
Dezhong Peng, Yi Zhang · 2006
Minor component analysis (MCA) has many important applications in signal processing and data analysis. Convergence is essential for MCA algorithms towards practical applications. This paper reviews an effective MCA algorithm and analyzes the convergence of this algorithm via deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee the convergence of this learning algorithm. Simulations are carried out to further illustrate the theoretical results achieved.