Principal component analysis

@article{Hess2018PrincipalCA,
  title={Principal component analysis},
  author={A. Hess and J. Hess},
  journal={Transfusion},
  year={2018},
  volume={58}
}
P rincipal component analysis (PCA) is an old statistical technique for identifying major relationships in complex data. PCA consists of creating artificial variables (“components”) optimized to maximize how much variation is explained in a data set. It is widely used in exploratory data analysis and predictive modeling. Its uses can be as simple as deconvoluting spectra to determine the concentrations of the individual chemicals in a mixture or as complex as trying to define the elements of a… Expand
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Article history: Received 22 June 2010 Accepted 14 September 2010 Available online 12 October 2010 Submitted by R.A. Brualdi AMS classification: 11Y05 15A23
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