Avijit Saha, Jim Christian, Dun-Sung Tang, Wu Chuan-Lin
We introduce oriented non-radial basis function networks (ONRBF) as a generalization of Radial Basis Function networks (RBF)- wherein the Euclidean distance metric in the exponent of the Gaussian is re(cid:173) placed by a more general polynomial. This permits the definition of more general regions and in particular- hyper-ellipses with orienta(cid:173) tions. In the case of hyper-surface estimation this scheme requires a smaller number of hidden units and alleviates the "curse of dimen(cid:173) sionality" associated kernel type approximators.In the case of an im(cid:173) age, the hidden units correspond to features in the image and the parameters associated with each unit correspond to the rotation, scal(cid:173) ing and translation properties of that particular "feature". In the con(cid:173) text of the ONBF scheme, this means that an image can be represented by a small number of features. Since, transformation of an image by rotation, scaling and translation correspond to identical transformations of the individual features, the ONBF scheme can be used to considerable advantage for the purposes of image recognition and analysis.