Efficient Approximate Search for Sets of Vectors
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We consider a similarity measure between two sets A and B of vectors, that balances the average and maximum cosine distance between pairs of vectors, one from set A and one from set B. As a motivation for this measure, we present lineage tracking in a database. To practically realize this measure, we need an approximate search algorithm that given a set of vectors A and sets of vectors B_1,...,B_n, the algorithm quickly locates the set B_i that maximizes the similarity measure. For the case where all sets are singleton sets, essentially each is a single vector, there are known efficient approximate search algorithms, e.g., approximated versions of tree search algorithms, locality-sensitive hashing (LSH), vector quantization (VQ) and proximity graph algorithms. In this work, we present approximate search algorithms for the general case. The underlying idea in these algorithms is encoding a set of vectors via a "long" single vector. The proposed approximate approach achieves significant performance gains over an optimized, exact search on vector sets.
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