Principal/Minor Component Analysis and Related Problems
Andrzej S Cichocki, Шун-ичи Амари · 2002
Neural networks with unsupervised learning algorithms organize themselves in such a way that they can detect or extract useful features, regularities, correlations of data or signals or separate or decorrelate some signals with little or no prior knowledge of the desired results. Normalized (constrained) Hebbian and anti-Hebbian learning rules are simple variants of basic unsupervised learning algorithms; in particular, learning algorithms for principal component analysis (PCA), singular value decomposition (SVD) and minor component analysis (MCA) belong to this class of unsupervised rules. Recently, many efficient and powerful adaptive algorithms have been developed for PCA, MCA and SVD and their extensions. The main objective of this chapter is a derivation and overview of the most important adaptive algorithms.