Classical RPCA (7 papers)
S. Engelen, M. Hubert, K. Vanden Branden, “A Comparison of Three Procedures for Robust PCA in High Dimensions”, Austrian Journal of Statistics, Volume 34, No. 2, pages 117-126, 2005.
R. Wilcox, "Robust principal components: A generalized variance perspective",Behavior Research Methods, Volume 40, Issue 1, pages 102-108, 2008.
C. Pascoal, M. Oliveira, A. Pacheco, R. Valadas, "Detection of outliers using robust principal component analysis: A simulation study", Soft Computing and Statistical Methods in Data Analysis", pages 499-507, 2010.
S. Sapra, "Robust vs. classical principal component analysis in the presence of outliers", Applied Economics Letters, Volume 17, No.6, 519-523, 2010.
E. Kotwa, "Robust procedures in chemometrics", PhD Thesis, DTU, Kongens Lyngby, Denmark, 2012.
B. De Ketelaere, M. Hubert, E. Schmitt, “Overview of PCA-based statistical process monitoring methods for time-dependent, high-dimensional data”, Journal of Quality Technology, 47, Volume 318–335, 2015.
S. Brodinova, T. Ortner, P. Filzmoser, M. Zaharieva, C. Breiteneder, "Evaluation of robust PCA for supervised audio outlier detection", Technical Report CS-2015-2, 2015.
D. Hong, L. Balzano, J. Fessler, “Towards a Theoretical Analysis of PCA for Heteroscedastic Data", Allerton Conference on Communication, Control, and Computing, Allerton 2016, pages 496–503, 2016.
Robust PCA via L+ S Decomposition (6 papers)
N. Vaswani, P. Narayanamurthy, "Static and Dynamic Robust PCA and Matrix Completion: A Review", Proceedings of IEEE, July 2018.
N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust Subspace Learning: Robust PCA, Robust Subspace Tracking and Robust Subspace Recovery”, IEEE Signal Processing Magazine, Volume 35, No. 4, pages 32-55, July 2018.
N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust PCA and Robust Subspace Tracking: A Comparative Evaluation”, IEEE Statistical Signal Processing Workshop, SSP 2018, Freiburg, Germany, June 2018.
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, Volume 23, pages 1-71, February 2017.
T. Bouwmans, N. Aybat, E. Zahzah, Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, May 2016.
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.