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Functional principal component model for high-dimensional brain imaging
We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vector...
Tallennettuna:
| Päätekijät: | , , , , , |
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| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
2011
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3169674/ https://ncbi.nlm.nih.gov/pubmed/21798354 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neuroimage.2011.05.085 |
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