Semantic Interaction provides a method for the machine to learn what features are important to the user. The process is a dialogue as the user expresses a configuration of the set that makes sense to them and the machine reciprocates by returning a new explanatory configuration that takes into account the user's interaction. Through repeated interactions, the machine and user learn from each other and converge to a weighted combination of high-dimensional attributes that explain the 'feature'.