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PETRELS-ADMM: Robust Subspace Tracking with Missing Data and Outliers

We propose a novel algorithm called 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. Particularly, we first use the ADMM solver for detecting outliers living in the measurement data in an efficient online way and then improve the well-known PETRELS algorithm to update the underlying subspace in the missing data context. The effectiveness of our algorithm, as compared to state-of-the-art algorithms, is illustrated on both simulated and real data.

Download

Our MATLAB code can be downloaded here.

Demo

+ Run the file DEMO_SEP_Main.m for simulated data.

+ Run the file DEMO_Video.m for real video data (Lobby data).

State-of-the-art algorithms for comparison

+ GRASTA: https://sites.google.com/site/hejunzz/grasta/

+ ROSETA: http://www.merl.com/research/license#ROSETA/

+ ReProCS: https://github.com/praneethmurthy/ReProCS/

+ NORST: https://github.com/praneethmurthy/NORST/

Some results

+ Simulated data: Matrix completion and performance comparison between PETRELS-ADMM against the state-of-the-art RST algorithms

+ Video background-foreground separation application

References

This code is free and open-source for research purposes. If you use this code, please acknowledge the following papers.

[1] L.T. Thanh, V.D. Nguyen, N. L. Trung, and K. Abed-Meraim. “Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee”. IEEE Trans. Signal Process. (TSP), 2021. [PDF].

[2] L.T. Thanh, V.D Nguyen, N.L. Trung and K. Abed-Meraim. “Robust Subspace Tracking with Missing Data and Outliers via ADMM”. European Signal Process. Conf. (EUSIPCO), 2019. [PDF].

Contact

LE Trung Thanh

AVITECH Institute
VNU University of Engineering and Technology, Vietnam

Email: thanhletrung@vnu.edu.vn // letrungthanhtbt@gmail.com

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