VNU-UET Repository

Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee

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

This is the latest version of this item.

Full text not available from this repository.

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.

Item Type: Article
Subjects: ISI-indexed journals
Divisions: Advanced Insitute of Engineering and Technology (AVITECH)
Depositing User: Lê Trung Thành
Date Deposited: 31 May 2021 10:58
Last Modified: 31 May 2021 10:58
URI: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4435

Available Versions of this Item

Actions (login required)

View Item View Item