EEMCS EPrints Service
Hendrikse, A.J. and Spreeuwers, L.J. and Veldhuis, R.N.J. (2009) A Bootstrap Approach to Eigenvalue Correction. In: Ninth IEEE International Conference on Data Mining, 2009. ICDM '09., 6-9 Dec. 2009, Miami Beach, FL, USA. pp. 818-823. IEEE Computer Society. ISSN 1550-4786 ISBN 978-1-4244-5242-2
Full text available as:
Official URL: http://dx.doi.org/10.1109/ICDM.2009.111
Eigenvalue analysis is an important aspect in many data modeling methods. Unfortunately, the eigenvalues of the sample covariance matrix (sample eigenvalues) are biased estimates of the eigenvalues of the covariance matrix of the data generating process (population eigenvalues). We present a new method based on bootstrapping to reduce the bias in the sample eigenvalues: the eigenvalue estimates are updated in several iterations, where in each iteration synthetic data is generated to determine how to update the population eigenvalue estimates. Comparison of the bootstrap eigenvalue correction with a state of the art correction method by Karoui shows that depending on the type of population eigenvalue distribution, sometimes the Karoui method performs better and sometimes our bootstrap method.
Export this item as:
To correct this item please ask your editor
Repository Staff Only: edit this item