All pairs similarity search is a problem where a set of data objects is given and the task is to find all pairs of objects that have similarity above a certain threshold for a given similarity measure-of-interest. When the number of points or dimensionality is high, standard solutions fail to scale gracefully. Approximate solutions such as Locality Sensitive Hashing (LSH) and its Bayesian variants (BayesLSH and BayesLSHLite) alleviate the problem to some extent and provide substantial speedup over traditional index based approaches. BayesLSH is used for pruning the candidate space and computation of approximate similarity, whereas BayesLSHLite can only prune the candidates, but similarity needs to be computed exactly on the original data. Thus where ever the explicit data representation is available and exact similarity computation is not too expensive, BayesLSHLite can be used to aggressively prune candidates and provide substantial speedup without losing too much on quality. However, the loss in quality is higher in the BayesLSH variant, where explicit data representation is not available, rather only a hash sketch is available and similarity has to be estimated approximately. In this work we revisit the LSH problem from a Frequentist setting and formulate sequential tests for composite hypothesis (similarity greater than or less than threshold) that can be leveraged by such LSH algorithms for adaptively pruning candidates aggressively. We propose a vanilla sequential probability ratio test (SPRT) approach based on this idea and two novel variants. We extend these variants to the case where approximate similarity needs to be computed using fixed-width sequential confidence interval generation technique. We compare these novel variants with the SPRT variant and BayesLSH/Bayes-LSHLite variants and show that they can provide tighter qualitative guarantees over BayesLSH/BayesLSHLite - a state-of-the-art approach - while being upto 2.1x faster than a traditional SPRT and 8.8x faster than AIIPairs.
