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)
- corpca.m: The function for CORPCA
- usageDemo_corpca.m: One demo to run CORPCA
- dataGeneration.m: Generating data for numerical simulations
- inexact_alm_rpca: This folder contains an offline RPCA (batch-based) code for initializing background and foreground prior information
Experimental results:
- videos: This folder consists of original test videos and separated sequences
- Please see file videos/videoList.md for more details
- fullRateComparisons_BootstrapCurtain.pptx: a presentation of video separation comparisons (Bootstrap and Curtain) for CORPCA vs. RPCA, GRASTA, and ReProCS with full data access
- compressiveRatesCORPCAvsReProCS_Bootstrap.pptx: a presentation of video separation comparisons (Bootstrap) for CORPCA vs. ReProCS with different measurement rates m/n
- compressiveRatesCORPCAvsReProCS_Curtain.pptx: a presentation of video separation comparisons (Curtain) for CORPCA vs. ReProCS with different measurement rates m/n