A robust subspace algorithm for principal component analysis
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Published on: February 16, 2024
Andreas Weingessel1, Kurt Hornik
1Institut für Statistik und Wahrscheinlichkeitstheorie, Technische Universität Wien, Vienna, Austria. Andreas.Weingessel@ci.tuwien.ac.at
Abstract:
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.
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