Density Sensitive Hashing

Low-Density Locality-Sensitive Hashing Boosts Metagenomic Binning.

Metagenomic binning is an essential task in analyzing metagenomic sequence datasets. To analyze structure or function of microbial communities from environmental samples, metagenomic sequence fragments are assigned to their taxonomic origins. Although sequence alignment algorithms, such as BWA, Bowtie or BLAST, can readily be used and usually provide high-resolution alignments and accurate binn...

Neighbor-Sensitive Hashing

Approximate kNN (k-nearest neighbor) techniques using binary hash functions are among the most commonly used approaches for overcoming the prohibitive cost of performing exact kNN queries. However, the success of these techniques largely depends on their hash functions’ ability to distinguish kNN items; that is, the kNN items retrieved based on data items’ hashcodes, should include as many true...

Beyond Locality-Sensitive Hashing

We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in R, our algorithm achieves Oc(n + d logn) query time and Oc(n + d logn) space, where ρ ≤ 7/(8c2) + O(1/c3) + oc(1). This is the first improvement over the result by Andoni and Indyk (FOCS 2006) and the first data structure that bypasses a locality-sensitive hashing lower boun...

Locality-Sensitive Hashing

Distance-sensitive hashing

We initiate the study of distance-sensitive hashing, a generalization of locality-sensitive hashing that seeks a family of hash functions such that the probability of two points having the same hash value is a given function of the distance between them. More precisely, given a distance space (X,dist) and a “collision probability function” (CPF) f : R→ [0, 1] we seek a distribution over pairs o...