eprintid: 4435
rev_number: 4
eprint_status: archive
userid: 381
dir: disk0/00/00/44/35
datestamp: 2021-05-31 10:58:07
lastmod: 2021-05-31 10:58:07
status_changed: 2021-05-31 10:58:07
type: article
succeeds: 4292
metadata_visibility: show
creators_name: Le, Trung Thanh
creators_name: Nguyen, Viet Dung
creators_name: Nguyen, Linh Trung
creators_name: Abed-Meraim, Karim
creators_id: letrungthanhtbt@gmail.com
creators_id: nvdung@vnu.edu.vn
creators_id: linhtrung@vnu.edu.vn
creators_id: karim.abed-meraim@univ-orleans.fr
title: Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee
ispublished: pub
subjects: isi
divisions: avitech
abstract: 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.
date: 2021-03-18
date_type: published
publisher: IEEE
official_url: https://ieeexplore.ieee.org/document/9381678
full_text_status: none
publication: IEEE Transactions on Signal Processing
volume: 69
pagerange: 2070 -2085
refereed: TRUE
issn: 1053-587X
citation:   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