Identifying Relevant Eigenimages - a Random Matrix Approach
Yu Ding, Yiu‐Cho Chung, Kun Huang, Orlando Paul Simonetti · arXiv (Cornell University) · 2008
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.