Distance Functions for Local PCA Methods
Alexander Kaiser, Wolfram Schenck, Ralf Möller · PUB – Publications at Bielefeld University (Bielefeld University) · 2010
The NGPCA method, a combination of the robust neural gas vector quantization method and a fast neural principal component analyzer, has proved to be a valuable tool for the generalized learning of high-dimensional data.At its core, the method uses a competitive ranking to adapt its units.The competition is guided by a specialized distance function -known as the normalized Mahalanobis distance -that assumes elliptic cluster shapes.Recently, an alternative distance function, the normalized Rayleigh quotient, has been suggested.This paper compares the performance of NGPCA on different distance functions.For the comparison a data set from a realistic robot arm experiment is used.