  We study Principal Component Analysis (PCA) in a setting where a part of the
corrupting noise is data-dependent and, as a result, the noise and the true
data are correlated. Under a bounded-ness assumption on the true data and the
noise, and a simple assumption on data-noise correlation, we obtain a nearly
optimal sample complexity bound for the most commonly used PCA solution,
singular value decomposition (SVD). This bound is a significant improvement
over the bound obtained by Vaswani and Guo in recent work (NIPS 2016) where
this "correlated-PCA" problem was first studied; and it holds under a
significantly weaker data-noise correlation assumption than the one used for
this earlier result.
