The Use of a Distance Measure in Regularised Discriminant Analysis
- 481 Downloads
Friedman (1989) proposed a regularised discriminant function (RDF) as a compromise between the normal-based linear and quadratic discriminant functions, by considering alternatives to the usual maximum likelihood estimates for the covariance matrices. These alternatives are characterised by two (regularisation) parameters, the values of which are customised to individual situations by jointly minimising a sample-based (cross-validated) estimate of future misclassification risk. This technique appears to provide considerable gains in classification accuracy in many circumstances, although it is computationally intensive.
Because of the computational burden inherent in the RDF, and with regards to criticisms of the technique by Rayens et al. (1991), we investigated whether information about appropriate values of the two regularisation parameters could be obtained from examining the behaviour of the Bhattacharyya distance between the various populations. This distance measure is found to give information which leads to unique and generally appropriate values for the regularisation parameters being selected.
KeywordsRegularised discriminant function regularisation parameter Bhattacharyya distance
- Koolaard, J. P., Lawoko, C. R. O. and Ganesalingam, S. (1996). Regularized discriminant (classification) analysis involving Bhattacharya distance measure. Proceedings of the 8th Australasian Remote Sensing Conference, Canberra, Australia (March 1996). Volume 2, Poster, pp 35–43.Google Scholar
- Koolaard, J. P.(1997). Some aspects of covariance regularisation in discriminant analysis. Unpublished PhD thesis, Massey University, New Zealand.Google Scholar