Structures of the covariance matrices in the classifier design
Structurization of the covariance matrices helps to reduce a number of parameters to be estimated. Own assumptions on the structure of the matrix are correct the structurization of the covariance matrix helps to reduce the generalization error in small learning-set cases. Efficacy of the matrix structurization increases if one decorrelates and scales the data, and uses the optimally stopped single layer perception classifier afterwards.
Index termsregularized discriminant analysis learning-set size generalization dimensionality covariance matrix parameters reduction single layer perception
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