relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4435/
title: Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee
creator: Le, Trung Thanh
creator: Nguyen, Viet Dung
creator: Nguyen, Linh Trung
creator: Abed-Meraim, Karim
subject: ISI-indexed journals
description: In this paper, we propose a novel algorithm, namely  PETRELS-ADMM, to deal with subspace tracking in the presence of outliers and missing data. The proposed approach consists of two main stages: outlier rejection and subspace estimation. In the first stage, alternating direction method of multipliers (ADMM) is effectively exploited to detect outliers affecting the observed data. In the second stage, we propose an improved version of the parallel estimation and tracking by recursive least squares (PETRELS) algorithm to update the underlying subspace in the missing data context. We then present a theoretical convergence analysis of PETRELS-ADMM which shows that it generates a sequence of subspace solutions converging to the optimum of its batch counterpart. The effectiveness of the proposed algorithm, as compared to state-of-the-art algorithms, is illustrated on both simulated and real data.
publisher: IEEE
date: 2021-03-18
type: Article
type: PeerReviewed
identifier:   Le, Trung Thanh and Nguyen, Viet Dung and Nguyen, Linh Trung and Abed-Meraim, Karim  (2021) Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee.  IEEE Transactions on Signal Processing, 69 .   2070 -2085.  ISSN 1053-587X     
relation: https://ieeexplore.ieee.org/document/9381678