Quadratic Bayes Normal Classifier (Bayes-Normal-2)
[W,R,S,M] = QDC(A,R,S,M)
Computation of the quadratic classifier between the classes of the dataset A assuming normal densities. R and S (0 <= R,S <= 1) are regularisation parameters used for finding the covariance matrix by
G = (1-R-S)*G + R*diag(diag(G)) + S*mean(diag(G))*eye(size(G,1))
This covariance matrix is then decomposed as G = W*W' + sigma^2 * eye(K), where W is a K x M matrix containing the M leading principal components and sigma^2 is the mean of the K-M smallest eigenvalues.
The use of soft labels is supported. The classification A*W is computed by NORMAL_MAP.
If R, S or M is NaN the regularisation parameter is optimised by REGOPTC. The best result are usually obtained by R = 0, S = NaN, M = , or by R = 0, S = 0, M = NaN (which is for problems of moderate or low dimensionality faster). If no regularisation is supplied a pseudo-inverse of the covariance matrix is used in case it is close to singular. It is better to avoid this and use always a small value for S, e.g. 1e-8.
1. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, 2nd edition, John Wiley and Sons, New York, 2001.
mappings, datasets, regoptc, nmc, nmsc, ldc, udc, quadrc, normal_map,