Robust Subspace Tracking With Contamination Mitigation via α-Divergence

Published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023

Paper Code

We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and it outperforms many RST methods while only having a low complexity linear to the data dimension. Some experiments were conducted to illustrate the performance of αFAPI.