Pca Consistency in High Dimension, Low Sample Size Context

@inproceedings{Jung2008PcaCI,
  title={Pca Consistency in High Dimension, Low Sample Size Context},
  author={Sungkyu Jung and J. S. Marron},
  year={2008}
}
Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows (i.e. High Dimension, Low Sample Size (HDLSS)) are becoming increasingly relevant. We investigate the asymptotic behavior of the Principal Component (PC) directions. HDLSS asymptotics are used to study consistency, strong inconsistency and subspace consistency. We show… CONTINUE READING

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