On Locality Sensitive Hashing for Sampling Extent Generators
In this article we introduce a method for sampling formal concepts using locality sensitive hashing (LSH). LSH is a technique used for finding approximate nearest neighbours given a set of hashing functions. Through our approach, we are able to predict the probability of an extent in the concept lattice given set of objects and their similarity index, a generalization of the Jaccard similarity between sets. Our approach allows defining a lattice-based amplification construction to design arbitrarily discriminative sampling settings.
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