Empirical Normalization for Quadratic Discriminant Analysis and Classifying Cancer Subtypes

@article{Kon2011EmpiricalNF,
  title={Empirical Normalization for Quadratic Discriminant Analysis and Classifying Cancer Subtypes},
  author={M. Kon and N. Nikolaev},
  journal={2011 10th International Conference on Machine Learning and Applications and Workshops},
  year={2011},
  volume={2},
  pages={374-379}
}
  • M. Kon, N. Nikolaev
  • Published 2011
  • Computer Science, Mathematics
  • 2011 10th International Conference on Machine Learning and Applications and Workshops
  • We introduce a new discriminant analysis method (Empirical Discriminant Analysis or EDA) for binary classification in machine learning. Given a dataset of feature vectors, this method defines an empirical feature map transforming the training and test data into new data with components having Gaussian empirical distributions. This map is an empirical version of the Gaussian copula used in probability and mathematical finance. The purpose is to form a feature mapped dataset as close as possible… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 24 REFERENCES
    Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
    • 11,226
    • PDF
    Discriminant Analysis and Statistical Pattern Recognition
    • 2,204
    • PDF
    Pattern Recognition and Machine Learning
    • 8,682
    • PDF
    Gene expression correlates of clinical prostate cancer behavior.
    • 2,316
    • PDF