  We perform a non-asymptotic analysis on the singular vector distribution
under Gaussian noise. In particular, we provide sufficient conditions on a
matrix for its first few singular vectors to have near normal distribution. Our
result can be used to facilitate the error analysis in linear dimension
reduction.
