Interlocking of learning and orthonormalization in RRLSA

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” (RRLSA) we show how the effort may be reduced by a factor of up to two by interlocking learning and the Gram-Schmidt method.