1. A method for classifying an anomaly in a digital image, the method comprising: receiving training data comprising a training feature value for each of a plurality of anomaly classification features for each of a plurality of training cases; defining a neighborhood size for each of a plurality of representation points in feature space for each anomaly classification feature based on the training data; receiving measured data comprising a measured feature value at an evaluation point in feature space for each anomaly classification feature for a measured case; determining a scale parameter vector for at least some of the representation points near the evaluation point for each anomaly classification feature to define a respective neighborhood size for that anomaly classification feature; determining a weight factor for the at least some of the representation points using the respective scale parameter vector; and applying the weight factor for the at least some of the representation points to the training data at the plurality of representation points to generate a classification probability for the anomaly for the measured case at the evaluation point, wherein the scale parameter vector for a respective anomaly classification feature indicates the respective neighborhood size used to generate the classification probability at the evaluation point in feature space.