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Compressed Online Dictionary Learning for Fast fMRI Decomposition

Abstract : We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest fMRI, we demonstrates that time-reduced dictionary learning produces result as reliable as non-reduced decompositions. We also show that this reduction significantly improves computational scalability.
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https://hal.archives-ouvertes.fr/hal-01271033
Contributor : Arthur Mensch <>
Submitted on : Monday, February 8, 2016 - 5:02:57 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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  • HAL Id : hal-01271033, version 1
  • ARXIV : 1602.02701

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Arthur Mensch, Gaël Varoquaux, Bertrand Thirion. Compressed Online Dictionary Learning for Fast fMRI Decomposition. International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. 2016. ⟨hal-01271033v1⟩

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