Dimension reduction via principal variables

@article{Cumming2007DimensionRV,
  title={Dimension reduction via principal variables},
  author={J. A. Cumming and David A. Wooff},
  journal={Computational Statistics & Data Analysis},
  year={2007},
  volume={52},
  pages={550-565}
}
For many large-scale datasets it is necessary to reduce dimensionality to the point where further exploration and analysis can take place. Principal variables are a subset of the original variables and preserve, to some extent, the structure and information carried by the original variables. Dimension reduction using principal variables is considered and a novel algorithm for determining such principal variables is proposed. This method is tested and compared with 11 other variable selection… CONTINUE READING
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