On the Robust PCA and Weiszfeld’s Algorithm

  • Sebastian Neumayer
  • Max NimmerEmail author
  • Simon Setzer
  • Gabriele Steidl


The principal component analysis (PCA) is a powerful standard tool for reducing the dimensionality of data. Unfortunately, it is sensitive to outliers so that various robust PCA variants were proposed in the literature. This paper addresses the robust PCA by successively determining the directions of lines having minimal Euclidean distances from the data points. The corresponding energy functional is non-differentiable at a finite number of directions which we call anchor directions. We derive a Weiszfeld-like algorithm for minimizing the energy functional which has several advantages over existing algorithms. Special attention is paid to carefully handling the anchor directions, where the relation between local minima and one-sided derivatives of Lipschitz continuous functions on submanifolds of \(\mathbb {R}^d\) is taken into account. Using ideas for stabilizing the classical Weiszfeld algorithm at anchor points and the Kurdyka–Łojasiewicz property of the energy functional, we prove global convergence of the whole sequence of iterates generated by the algorithm to a critical point of the energy functional. Numerical examples demonstrate the very good performance of our algorithm.


Robust principal component analysis PCA Robust subspace recovery Weiszfeld algorithm Kurdyka-Lojasiewicz property 



Funding by the German Research Foundation (DFG) within the Research Training Group 1932, project area P3, is gratefully acknowledged.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Sebastian Neumayer
    • 1
  • Max Nimmer
    • 1
    Email author
  • Simon Setzer
    • 3
  • Gabriele Steidl
    • 1
    • 2
  1. 1.Department of MathematicsTechnische Universität KaiserslauternKaiserslauternGermany
  2. 2.Fraunhofer ITWMKaiserslauternGermany
  3. 3.Engineers GateLondonUK

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