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A method to build a representation using a classifier and its use in a K Nearest Neighbors-based deployment

The K Nearest Neighbors (KNN) is strongly dependent on the quality of the distance metric used. For supervised classification problems, the aim of metric learning is to learn a distance metric for the input data space from a given collection of pair of similar/dissimilar points. A crucial point is the distance metric used to measure the closeness of instances. In the industrial context of this paper the key point is that a very interesting source of knowledge is available : a classifier to be deployed. The knowledge incorporated in this classifier is used to guide the choice (or the construction) of a distance adapted to the situation Then a KNN-based deployment is elaborated to speed up the deployment of the classifier compared to a direct deployment.