• DOI:10.1198/004017004000000563
Corpus ID: 5071469

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

View on Taylor & Francis

stat.osu.edu

Save to Library

Create Alert

Cite

Launch Research Feed