Estimating principal components of large covariance matrices using the Nyström method

@article{Arcolano2011EstimatingPC,
  title={Estimating principal components of large covariance matrices using the Nystr{\"o}m method},
  author={Nicholas Arcolano and Patrick J. Wolfe},
  journal={2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2011},
  pages={3784-3787}
}
Covariance matrix estimates are an essential part of many signal processing algorithms, and are often used to determine a low-dimensional principal subspace via their spectral decomposition. However, for sufficiently high-dimensional matrices exact eigen-analysis is computationally intractable, and in the case of limited data, sample eigenvalues and eigenvectors are known to be poor estimators of their true counterparts. To address these issues, we propose a covariance estimator that is… CONTINUE READING
7 Citations
12 References
Similar Papers

Similar Papers

Loading similar papers…