Smooth Principal Component Analysis over two-dimensional manifolds with an application to Neuroimaging

@article{Lila2016SmoothPC,
  title={Smooth Principal Component Analysis over two-dimensional manifolds with an application to Neuroimaging},
  author={Eardi Lila and John A. D. Aston and Laura M. Sangalli},
  journal={arXiv: Applications},
  year={2016}
}
Motivated by the analysis of high-dimensional neuroimaging signals located over the cortical surface, we introduce a novel Principal Component Analysis technique that can handle functional data located over a two-dimensional manifold. For this purpose a regularization approach is adopted, introducing a smoothing penalty coherent with the geodesic distance over the manifold. The model introduced can be applied to any manifold topology, can naturally handle missing data and functional samples… 
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45 Citations
Background Citations
16
Methods Citations
16

45 Citations

Statistical Analysis of Functions on Surfaces, With an Application to Medical Imaging
  • E. Lila, J. Aston
  • Mathematics, Computer Science
    Journal of the American Statistical Association
  • 2019
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