Interlocking of learning and orthonormalization in RRLSA

Abstract

In sequential principal component analyzers based on deflation of the input vector, deviations from orthogonality of the previous eigenvector estimates may entail a severe loss of orthogonality in the next stages. A combination of the learning method with subsequent Gram–Schmidt orthonormalization solves this problem, but increases the computational effort. For the “robust recursive least squares learning algorithm” we show how the effort may be reduced by a factor of up to two by interlocking learning and the Gram–Schmidt method.