Necessary and sufficient conditions for convergence of the DDT systems of the Normalized PAST algorithms
We propose a refined DDT analysis for Normalized PAST algorithms. The DDT analysis clarified necessary and sufficient conditions for the convergence. The range of the forgetting factor, for MCA, is doubled from the first DDT analysis. The range of the forgetting factor, for PCA, is extended to the full range (0, 1]. With the forgetting factor in extended ranges, the convergence can be accelerated.
normalized projection approximation subspace tracking (normalized past) algorithm deterministic discrete time (ddt) approach principal component minor component principal component analysis (pca) learning algorithm minor component analysis (mca) learning algorithm