Representing and Learning Variations

In machine learning, objects are usually grouped according to similarities found in the objects descriptions. Recent works, however, suggest that representing the differences between object descriptions is also pertinent in many learning tasks. But not much study has been made on how to represent and learn from differences. This paper proposes a qualitative representation of inter-object variations that can be used as input of a learning task. The main idea is to define inter-objects variations as attributes of repetitions of objects, so that machine learning methods will be able to manipulate them in the same way as they manipulate object attributes. The approach is tested on both classification and a numerical value prediction tasks and shows encouraging results.