TY  - RPRT
CY  - Vietnam
N1  - UET-AVITECH-2019003
ID  - SisLab3702
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3702/
A1  - Le, Trung Thanh
A1  - Nguyen, Viet Dung
A1  - Nguyen, Linh Trung
A1  - Karim, Abed Meraim
Y1  - 2019/05//
N2  - 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.
PB  - VNU University of Engineering and Technology, Vietnam National University, Hanoi
VL  - UET-AVITECH-2019003
M1  - technical_report
TI  - Robust subspace tracking: Novel algorithm and performance guarantee
AV  - none
EP  - 39
ER  -