R1-PCA
D. Ding, D. Zhou, X. He, H. Zha, "R1-PCA: rotational invariant l1-norm principal component analysis for robust subspace factorization", International Conference on Machine Learning, ICML 2006, pages 281–288, 2006.
PCA-l1
N. Kwak, "Principal component analysis based on l1-norm maximization", IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1672–1680, 2008.
l1-PCA
S. Chamadia D. Pados, "Outlier processing via l1-principal subspaces", Florida Artificial Intelligence Research Society, FLAIRS 2017, May 2017.
M. Dhanaraj, P. Markopoulos, "Novel algorithm for incremental l1-norm principal-component analysis", European Signal Processing Conference, EUSIPCO 2018, September 2018.
M. Dhanaraj, "Incremental and Adaptive l1-Norm Principal Component Analysis: Novel Algorithms and Applications", PhD Thesis, Rochester Institute of Technology", 2018.
P. Markopoulos, M. Dhanaraj, A. Savakis, "Adaptive l1-norm Principal-Component Analysis with Online Outlier Rejection", IEEE Journal of Selected Topics in Signal Processing, December 2018.
Y. Liu, Z. Bellay, P. Bradsky, G. Chandler, B. Craig, "Edge-to-fog computing for color-assisted moving object detection", SPIE Big Data: Learning, Analytics, and Applications, May 2019.
Half-Quadratic-PCA (HQ-PCA)
R. He, B. Hu, W. Zheng, X. Kong, "Robust principal component analysis based on maximum correntropy criterion", IEEE Transactions on Image Processing, pages 1485–1494, 2011.
Pursuing Dynamic Spatio-Temporal Models (STDM)
Y. Xu, "Moving Object Segmentation by Pursuing Local Spatio-Temporal Manifold", Technical Report, Sun Yat-Sen University, China, 2012.
L. Lin, Y. Xu, X. Liang, "Complex background subtraction by pursuing dynamic spatio-temporal manifolds", IEEE Transactions on Image Processing, 2013.