Principal component analysis of 1 / f α noise

@inproceedings{Gao2003PrincipalCA,
  title={Principal component analysis of 1 / f α noise},
  author={J. B. Gao and Yinhe Cao and Jae-Min Lee},
  year={2003}
}
Principal component analysis (PCA) is a popular data analysis method. One of the motivations for using PCA in pr to reduce the dimension of the original data by projecting the raw data onto a few dominant eigenvectors with large (energy). Due to the ubiquity of 1 /f α noise in science and engineering, in this Letter we study the prototypical stoc model for 1/f α processes—the fractional Brownian motion (fBm) processes using PCA, and find that the eigenvalu PCA of fBm processes follow a power… CONTINUE READING
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