Approximate Furthest Neighbor with Application to Annulus Query.
Inf. Syst.(2017)
摘要
Much recent work has been devoted to approximate nearest neighbor queries. Motivated by applications in recommender systems, we consider approximate furthest neighbor (AFN) queries and present a simple, fast, and highly practical data structure for answering AFN queries in high-dimensional Euclidean space. The method builds on the technique of Indyk (SODA 2003), storing random projections to provide sublinear query time for AFN. However, we introduce a different query algorithm, improving on Indyk's approximation factor and reducing the running time by a logarithmic factor. We also present a variation based on a query-independent ordering of the database points; while this does not have the provable approximation factor of the query-dependent data structure, it offers significant improvement in time and space complexity. We give a theoretical analysis and experimental results. As an application, the query-dependent approach is used for deriving a data structure for the approximate annulus query problem, which is defined as follows: given an input set S and two parameters r 0 and w ź 1 , construct a data structure that returns for each query point q a point p ź S such that the distance between p and q is at least r / w and at most wr . HighlightsPresents the approximate furthest neighbor problem (AFN).Gives sub-linear time solutions to AFN based on random projections.Presents the approximate annulus query problem (AAQ).Gives a sub-linear time solution to AAQ based on random projections and locality sensitive hashing.
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