Different Problem Formulations

The different problem formulations for robust subspace learning/tracking frameworks which are based on decomposition into low-rank plus additive matrices are the following ones:

• Sparse Dictionary Learning (17 papers)
• Sparse Linear Approximation (7 papers)
• Compressing Sensing (16 papers)

Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. Rochelle, France.

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As this website gives many information that come from my research, please cite my following survey papers:

T. Bouwmans . A. Sobral, S. Javed, S. Jung, E. Zahzah, "Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset", Computer Science Review, November 2016. [pdf]

T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]

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