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Modified sparse functional principal component analysis for fMRI data process

Modified sparse functional principal component analysis for fMRI data process Sparse and functional principal component analysis is a technique to extract sparse and smooth principal components from a matrix. In this paper, we propose a modified sparse and functional principal component analysis model for feature extraction. We measure the tuning parameters by their robustness against random perturbation, and select the tuning parameters by derivative-free optimization. We test our algorithm on the ADNI dataset to distinguish between the patients with Alzheimer's disease and the control group. By applying proper classification methods for sparse features, we get better result than classic singular value decomposition, support vector machine and logistic regression. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Modified sparse functional principal component analysis for fMRI data process

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Modified sparse functional principal component analysis for fMRI data process

Abstract

Sparse and functional principal component analysis is a technique to extract sparse and smooth principal components from a matrix. In this paper, we propose a modified sparse and functional principal component analysis model for feature extraction. We measure the tuning parameters by their robustness against random perturbation, and select the tuning parameters by derivative-free optimization. We test our algorithm on the ADNI dataset to distinguish between the patients with Alzheimer's...
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Publisher
Taylor & Francis
Copyright
© 2019 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2019.1591072
Publisher site
See Article on Publisher Site

Abstract

Sparse and functional principal component analysis is a technique to extract sparse and smooth principal components from a matrix. In this paper, we propose a modified sparse and functional principal component analysis model for feature extraction. We measure the tuning parameters by their robustness against random perturbation, and select the tuning parameters by derivative-free optimization. We test our algorithm on the ADNI dataset to distinguish between the patients with Alzheimer's disease and the control group. By applying proper classification methods for sparse features, we get better result than classic singular value decomposition, support vector machine and logistic regression.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2019

Keywords: Sparse; functional; principal component analysis; fMRI

References

  • A road to classification in high dimensional space: the regularized optimal affine discriminant
  • Sparsifying the fisher linear discriminant by rotation
  • The analysis of two-way functional data using two-way regularized singular value decompositions
  • Finding optimal algorithmic parameters using derivative-free optimization
  • Alzheimer's Disease Neuroimaging Initiative. Link clustering to explore brain dynamics using resting state functional MRI
  • Functional network organization of the human brain