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Identifying Relevant Eigenimages - a Random Matrix Approach
- Publication Year :
- 2008
-
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.<br />Comment: 7 pages, 5 figures
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.0812.4618
- Document Type :
- Working Paper