A robust subspace algorithm for principal component analysis.
We present a noise robust PCA algorithm which is an extension of the Oja subspace algorithm and allows tuning the noise sensitivity. We derive a loss function which is minimized by this algorithm and interpret it in a noisy PCA setting. Results on the local stability analysis of this algorithm are given and it is shown that the locally stable equilibria are those which minimize the loss function.
Weingessel
Andreas
A
Institut für Statistik und Wahrscheinlichkeitstheorie, Technische Universität Wien, Vienna, Austria. Andreas.Weingessel@ci.tuwien.ac.at
Hornik
Kurt
K
D000465:Algorithms; D006801:Humans; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted