Online Robust Principal Component Analysis with Change Point Detection
This provides an efficient solution for real-time data processing in applications like surveillance, though it is incremental as it builds on existing robust PCA methods.
The paper tackles the inefficiency of batch robust PCA methods for big data by developing an online moving window robust PCA (OMWRPCA) that tracks both slowly and abruptly changing subspaces and detects change points, demonstrating superior performance in simulations and real-time video background subtraction.
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.