  In this paper, we propose an acceleration scheme for online memory-limited
PCA methods. Our scheme converges to the first $k>1$ eigenvectors in a single
data pass. We provide empirical convergence results of our scheme based on the
spiked covariance model. Our scheme does not require any predefined parameters
such as the eigengap and hence is well facilitated for streaming data
scenarios. Furthermore, we apply our scheme to challenging time-varying systems
where online PCA methods fail to converge. Specifically, we discuss a family of
time-varying systems that are based on Molecular Dynamics simulations where
batch PCA converges to the actual analytic solution of such systems.
