2. The method of claim 1 , wherein applying the at least one heuristic to the training data includes applying a heuristic comprising: for each positive label group, computing a representative score vector based on the score vectors of the digital objects in that positive label group; computing a first distance from a first score vector of a digital object of a first of the positive label groups to the representative score vector of the first of the positive label groups; computing a second distance from the first score vector to the representative score vector of a second of the positive label groups; and comparing the first distance to the second distance.
2. The method of claim 1 , wherein applying the at least one heuristic to the training data includes applying a heuristic comprising: for each positive label group, computing a representative score vector based on the score vectors of the digital objects in that positive label group; computing a first distance from a first score vector of a digital object of a first of the positive label groups to the representative score vector of the first of the positive label groups; computing a second distance from the first score vector to the representative score vector of a second of the positive label groups; and comparing the first distance to the second distance. 4. The method of claim 2 , wherein the first distance and the second distance are Euclidean distances.