Identifying Relevant Eigenimages - a Random Matrix Approach

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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.
7 pages, 5 figures

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Data Analysis, Statistics and Probability, Disordered Systems and Neural Networks, Medical Physics, Machine Learning

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http://salesiana.dossiersoluciones.com/handle/123456789/213775

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0812.4618
 http://arxiv.org/abs/0812.4618

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arXiv