Locality-sensitive hashing

About: Locality-sensitive hashing is a research topic. Over the lifetime, 1894 publications have been published within this topic receiving 69362 citations.

FRESH: Fréchet Similarity with Hashing

Abstract: This paper studies the r-range search problem for curves under the continuous Frechet distance: given a dataset S of n polygonal curves and a threshold \(r>0\), construct a data structure that, for any query curve q, efficiently returns all entries in S with distance at most r from q. We propose FRESH, an approximate and randomized approach for r-range search, that leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications. We experimentally compare FRESH to exact and deterministic solutions, and we show that high performance can be reached by suitably relaxing precision and recall.

Combinatorial group testing for corruption localizing hashing

Abstract: Corruption-localizing hashing is a recently introduced cryptographic primitive that enhances the well-known primitive of collision-intractable hashing. In addition to allowing detection of changes in input data, they also provide a superset of the changes location, where the accuracy of this superset is formalized as a metric, called localization factor. In this paper we consider the problem of designing corruption-localizing hash schemes with reduced localization factor. We define a new and natural notion of localizing codes, and prove that any such code can be used in conjunction with collision-intractable hashing, to obtain corruption-localizing hashing, a general result of independent interest. Then we propose two localizing codes based on combinatorial group testing techniques (i.e., superimposed codes), resulting in the first corruption-localizing hash scheme with constant localization factor against an unbounded number of corruptions of distinct and unbounded lengths.

LSH (Locality Sensitive Hashing)-based clustering and indexing method and LSH-based clustering and indexing system

Abstract: The invention relates to an LSH (Locality Sensitive Hashing)-based clustering and indexing method and an LSH-based clustering and indexing system. The LSH-based clustering and indexing method comprises the steps of step 1, carrying out clustering analysis on a data set, dividing the data set into a plurality of categories, and determining and ensuring a clustering center of each category; step 2, establishing a hashing table in each category by adopting an LSH method; step 3, calculating Euclidean distance between each clustering center and a query point, and selecting multiple categories in minimum Euclidean distances as candidate categories; step 4, calculating a hashing value of the query point in each candidate category, and selecting data points of which the hashing values are the same as that of the query point in the candidate categories as candidate points according to the hashing table established in step 2; step 5, calculating the Euclidean distances between the candidate points and the query point, and taking the candidate point in minimum Euclidean distance as a nearest adjacent point to the query point. According to the LSH-based clustering and indexing method and the LSH-based clustering and indexing system, disclosed by the invention, great increasing of query efficiency and relative stability of query performance can be obtained under the situation of less sacrificing the accuracy rate.

Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)

Abstract: Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In the prior work, the authors use asymmetric transformations which convert the problem of approximate MIPS into the problem of approximate near neighbor search which can be efficiently solved using hashing. In this work, we provide a different transformation which converts the problem of approximate MIPS into the problem of approximate cosine similarity search which can be efficiently solved using signed random projections. Theoretical analysis show that the new scheme is significantly better than the original scheme for MIPS. Experimental evaluations strongly support the theoretical findings.