Higher order autocorrelations for pattern classification

The use of higher-order local autocorrelations as features for pattern recognition has been acknowledged for many years, but their applicability was restricted to relatively low orders (2 or 3) and small local neighborhoods, due to combinatorial increase in computational costs. A new method for using these features is presented, which allows the use of autocorrelations of any order and of larger neighborhoods. The method is closely related to the classifier used, a support vector machine (SVM), and exploits the special form of the inner products of autocorrelations and the properties of some kernel functions used by SVM. Using SVM, linear and nonlinear classification functions can be learned, extending the previous works on higher-order autocorrelations which were based on linear classifiers.