Identifying Relevant Eigenimages - a Random Matrix Approach

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dc.creatorDing, Yu
dc.creatorChung, Yiu-Cho
dc.creatorHuang, Kun
dc.creatorSimonetti, Orlando P.
dc.date2008-12-25
dc.date.accessioned2026-07-07T12:22:48Z
dc.date.available2026-07-07T12:22:48Z
dc.descriptionDimensional 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.
dc.description7 pages, 5 figures
dc.identifierhttps://arxiv.org/abs/0812.4618
dc.identifierhttp://arxiv.org/abs/0812.4618
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/213775
dc.subjectData Analysis, Statistics and Probability
dc.subjectDisordered Systems and Neural Networks
dc.subjectMedical Physics
dc.subjectMachine Learning
dc.titleIdentifying Relevant Eigenimages - a Random Matrix Approach
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