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Application of the optimal brain surgeon pruning strategy to a real-aperture radar detection algorithm

We have applied the Optimal Brain Surgeon (OBS) pruning strategy to a polynomial discriminator in order to reduce the number of coefficients it employs. The polynomial discriminator multiplies various combinations of test features by the respective coefficients and then sums the products to obtain a discriminant that is compared to a threshold. The test features are derived from the radar data associated with the cell under test, while the coefficients are determined a priori by minimizing the mean-squared error (MSE) between the actual and the desired value of the discriminant over the training set. The OBS pruning strategy examines the Hessian matrix of a network's error surface-- derived from the training data--to determine which coefficients can be eliminated without adversely affecting the MSE. Besides simplifying the network, such a reduction may also allow for improved network performance when an unseen test data is input. We present the application of the OBS pruning strategy to reduce the dimensionality of a polynomial discriminator and show that the reduction in dimensionality does not adversely affect performance.