High Dimensional Discriminant Analysis

@inproceedings{Bouveyron2005HighDD,
  title={High Dimensional Discriminant Analysis},
  author={Charles Bouveyron and St{\'e}phane Girard and Cordelia Schmid},
  year={2005}
}
We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and regularizes class conditional covariance matrices in order to adapt the Gaussian framework to high dimensional data. This regularization is achieved by assuming that classes are spherical in their eigenspace… CONTINUE READING
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Showing 1-9 of 9 references

High dimensional discriminant analysis

  • Bouveyron et al, 2005C. Bouveyron, S. Girard, C. Schmid
  • Technical Report 5470,
  • 2005

The Elements of Statistical Learning

  • Hastie et al, 2001T. Hastie, R. Tibshirani, J. Friedman
  • 2001

Common principal components in k groups

  • Flury, 1984B.W. Flury
  • Journal of the American Statistical Association,
  • 1984

Probability density estimation in higher dimensions

  • Scott, Thompson, 1983D. Scott, J. Thompson
  • In Proceedings of the Fifteenth Symposium on the…
  • 1983

Flury . Common principal components in k groups

  • W. B.
  • Journal of the American Statistical Association

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