Support objects for domain approximation

We propose a novel algorithm for extracting samples from a data set supporting the extremal points in the set. Since the density of the data set is not taken into account, the method could enable adaptation to novel (e.g. machine wear) data. Knowledge about the clustering structure of the data can aid in determination of the complexity of the solution. The algorithm is evaluated on its computational feasibility and performance with progressively more dissimilar data.

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