Skip to Main content Skip to Navigation
Journal articles

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

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 consistsof 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.
Document type :
Journal articles
Complete list of metadata

https://hal-ensta-bretagne.archives-ouvertes.fr/hal-03212505
Contributor : Marie Briec <>
Submitted on : Monday, May 3, 2021 - 5:34:41 PM
Last modification on : Wednesday, May 5, 2021 - 3:41:16 AM

File

Manuscript.pdf
Files produced by the author(s)

Identifiers

Citation

Le Trung Thanh, Viet Dung Nguyen, Nguyen Linh Trung, Karim Abed-Meraim. Robust Subspace Tracking With Missing Data and Outliers: Novel Algorithm With Convergence Guarantee. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2021, 69, pp.2070-2085. ⟨10.1109/TSP.2021.3066795⟩. ⟨hal-03212505⟩

Share

Metrics

Record views

66

Files downloads

22