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.