A class of PCA learning algorithms and their convergence
Yu Zhang · 2010
This paper proposes a class of principal component analysis (PCA) learning algorithms with constant learning rates. It will prove via deterministic discrete time (DDT) method that these PCA learning algorithms are globally convergent.