  Dimensional reduction of high dimensional data can be achieved by keeping
only the relevant eigenmodes after principal component analysis. However,
differentiating relevant eigenmodes from the random noise eigenmodes is
problematic. A new method based on the random matrix theory and a statistical
goodness-of-fit test is proposed in this paper. It is validated by numerical
simulations and applied to real-time magnetic resonance cardiac cine images.
