Abstract
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.