In article <[EMAIL PROTECTED]>, Fernando De la Torre <[EMAIL PROTECTED]> writes >Hi, > >I am performing discriminant analysis (Classical fisher analysis) with very >high dimensional data. >I do not have enough data to even have a full rank estimation of the >covariance matrix. >Do you know some relate research on computing eigenvectors of covariance >matrices which are not full rank and if there exist some asymptotical >properties? >
I think this is a good place to start: [48] Bor-Chen Kuo and David A. Landgrebe, "A Covariance Estimator for Small Sample Size Classification Problems and Its Application to Feature Extraction,"IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 4, pp 814-819, April 2002. available from http://dynamo.ecn.purdue.edu/~landgreb/publications.html It seems no-one writes an article on this subject without referring to: [3] J.H. Friedman, Regularized Discriminant Analysis, Journal of the American Statistical Association, vol. 84, pp. 165-175, March 1989 -- Graham Jones http://www.visiv.co.uk Emails to [EMAIL PROTECTED] may be deleted as spam Please add a j just before the @ to ensure delivery . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
