2017/02/19 by Wei Xiao, Xiaolin Huang, Xiao, Wei +7 · 1 citation
Chemistry · Computer Science · Mathematics · #Advanced Statistical Methods and Models #Applications (stat.AP) #Blind Source Separation Techniques #Computation (stat.CO) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.1702.05698
openalex publication_date 2017/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this paper, we develop an efficient online robust principal component methods, namely online moving window robust principal component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can successfully track not only slowly changing subspace but also abruptly changed subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can detect change points of the underlying subspaces. Extensive simulation studies demonstrate the superior performance of OMWRPCA compared with other state-of-art approaches. We also apply the algorithm for real-time background subtraction of surveillance video.