andrewssobral / corpca

Compressive Online Robust Principal Component Analysis with Multiple Prior Information

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CORPCA

Compressive Online Robust Principal Component Analysis with Multiple Prior Information (CORPCA)

Version 1.1,  Jan. 24, 2017
Implementations by Huynh Van Luong, Email: huynh.luong@fau.de,
Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg.  

Please see LICENSE for the full text of the license.

Please cite this publication:

Huynh Van Luong, N. Deligiannis, J. Seiler, S. Forchhammer, and A. Kaup, "Compressive Online Robust Principal Component Analysis with Multiple Prior Information," in IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017), e-print in arXiv, Montreal, Canada, Nov. 2017.

Solving the problem

Inputs:

  • : A vector of observations/data
  • : A measurement matrix
  • : The foreground prior
  • : A matrix of the background prior, which could be initialized by previous backgrounds

Outputs:

  • : Estimates of foreground and background
  • : The updated foreground prior
  • : The updated background prior

Source code files: (for C++ codes, please refer to corpca-of)

Experimental results:

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Compressive Online Robust Principal Component Analysis with Multiple Prior Information

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