ROBPCA: A New Approach to Robust Principal Component Analysis

@article{Hubert2005ROBPCAAN,
  title={ROBPCA: A New Approach to Robust Principal Component Analysis},
  author={M. Hubert and P. Rousseeuw and K. V. Branden},
  journal={Technometrics},
  year={2005},
  volume={47},
  pages={64 - 79}
}
  • M. Hubert, P. Rousseeuw, K. V. Branden
  • Published 2005
  • Mathematics, Computer Science
  • Technometrics
  • We introduce a new method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance matrix of the data and hence is highly sensitive to outlying observations. Two robust approaches have been developed to date. The first approach is based on the eigenvectors of a robust scatter matrix such as the minimum covariance determinant or an S-estimator and is limited to relatively low-dimensional data. The second approach is based on projection pursuit and can… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 45 REFERENCES
    Robust PCA and classification in biosciences
    • 158
    • PDF
    A fast method for robust principal components with applications to chemometrics
    • 217
    Fast cross-validation in robust PCA
    • 10
    High breakdown estimators for principal components: the projection-pursuit approach revisited
    • 256
    A robust PCR method for high‐dimensional regressors
    • 76
    Fast and robust discriminant analysis
    • 202
    • PDF
    LIBRA: a MATLAB library for robust analysis
    • 354
    • PDF