Our context of interest is how binary locality sensitive hash (LSH) functions can be used to solve the approximate near neighbour (ANN) problem, which seeks to find the k closest elements of some dataset X to some further point q presented as a query. Binary locality sensitive function families H are sets of functions each which accept a point and return a binary value. A function is locality sensitive if and only if the output of the function is more likely to be equal (a 'hash collision') if two close vectors are used as input than if two far vectors are used. A data structure can be built by generating binary hash codes for each member of X, which are generated by drawing and applying one or more functions from H. When q is presented as a query, the same set of functions is applied to it and those elements of X with equal binary hash codes are retrieved. In this paper we introduce dynamic query modification. This process changes q at query time to form a new value c, which by theoretical and experimental analysis we prove has two significant advantages. Firstly, the hash output of c collides with near neighbours with a greater probability than q. Secondly, we show there is little chance of c failing to collide with any near neighbours; a property which we demonstrate is not true for q. To demonstrate the efficacy of the technique, we define a novel structure MQ-Forest, a modified version of RP-Forest. Both are binary LSH-based ANN mechanisms, but MQ-Forest dynamically estimates a value for c during the query process. We show that MQ-Forest reduces both build and query times by up to 40% when measured over several large, high-dimensional benchmark datasets.

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