Le, Trung Thanh and Nguyen, Viet Dung and Nguyen, Linh Trung and Abed-Meraim, Karim
(2020)
Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee.
IEEE Transactions on Signal Processing
.
ISSN 1053-587X
(In Press)
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.
Available Versions of this Item
-
Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee. (deposited 20 Dec 2020 15:01)
[Currently Displayed]
Actions (login required)
 |
View Item |
