@techreport{SisLab3702,
          number = {UET-AVITECH-2019003},
           month = {May},
          author = {Trung Thanh Le and Viet Dung Nguyen and Linh Trung Nguyen and Abed Meraim Karim},
            note = {UET-AVITECH-2019003},
            type = {Technical Report},
         address = {Vietnam},
           title = {Robust subspace tracking: Novel algorithm and performance guarantee},
       publisher = {VNU University of Engineering and Technology, Vietnam National University, Hanoi},
            year = {2019},
     institution = {VNU University of Engineering and Technology, Vietnam National University, Hanoi},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3702/},
        abstract = {Subspace tracking, which is refered to online PCA, is a classical problem in signal processing with various applications in wireless communications, rada and image/video processing. Since outliers and missing data are ubiquitous and more common in big data regime, robust variants of subspace tracking (RST) are crucial. In this paper, we propose a novel algorithm, namely PETRELSADMM, to improve RST performance in such scenario. The proposed approach consists of two main stages, including outlier rejection and subspace estimation. In the first stage, alternating direction method of multipliers (ADMM) solver is used to detect outliers residing in the observed data in an efficient way. In the second stage, we propose a modification of the parallel estimation and tracking by recursive least squares (PETRELS) algorithm to update the underlying subspace. A theoretical convegence analysis is provided, i.e., we prove that PETRELS-ADMM can generate a sequence of subspace solutions converging to the optimum of its batch counterpart. Performance studies show the superiority of our algorithms as compared to the state-of-the-art algorithms on both synthesis data and real data.}
}