Identifying Relevant Eigenimages - a Random Matrix Approach
Data Analysis, Statistics and Probability
2008-12-31 v1 Disordered Systems and Neural Networks
Medical Physics
Machine Learning
Abstract
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.
Cite
@article{arxiv.0812.4618,
title = {Identifying Relevant Eigenimages - a Random Matrix Approach},
author = {Yu Ding and Yiu-Cho Chung and Kun Huang and Orlando P. Simonetti},
journal= {arXiv preprint arXiv:0812.4618},
year = {2008}
}
Comments
7 pages, 5 figures