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Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging

Abstract : Various metrics for comparing diffusion tensors have been recently proposed in the literature. We consider a broad family of metrics which is indexed by a single power parameter. A likelihood-based procedure is developed for choosing the most appropriate metric from the family for a given dataset at hand. The approach is analogous to using the Box-Cox transformation that is frequently investigated in regression analysis. The methodology is illustrated with a simulation study and an application to a real dataset of diffusion tensor images of canine hearts.
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https://hal.inria.fr/hal-00813769
Contributor : Project-Team Asclepios <>
Submitted on : Thursday, May 2, 2013 - 5:03:29 PM
Last modification on : Monday, August 31, 2020 - 1:06:02 PM

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  • HAL Id : hal-00813769, version 1
  • ARXIV : 1009.3045

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I. L. Dryden, Xavier Pennec, Jean-Marc Peyrat. Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging. 2010. ⟨hal-00813769⟩

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