Robust PCA: Optimization of the Robust Reconstruction Error Over the Stiefel Manifold

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)


It is well known that Principal Component Analysis (PCA) is strongly affected by outliers and a lot of effort has been put into robustification of PCA. In this paper we present a new algorithm for robust PCA minimizing the trimmed reconstruction error. By directly minimizing over the Stiefel manifold, we avoid deflation as often used by projection pursuit methods. In distinction to other methods for robust PCA, our method has no free parameter and is computationally very efficient. We illustrate the performance on various datasets including an application to background modeling and subtraction. Our method performs better or similar to current state-of-the-art methods while being faster.


Principal Component Analysis Reconstruction Error Breakdown Point Robust Principal Component Analysis Ground Truth Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



M.H. has been partially supported by the ERC Starting Grant NOLEPRO and M.H. and S.S. have been partially supported by the DFG Priority Program 1324, “Extraction of quantifiable information from complex systems”.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.INRIA – Sierra Project-TeamÉcole Normale SupérieureParisFrance
  2. 2.Computer Science DepartmentSaarland UniversitySaarbrückenGermany

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