Locality-sensitive hashing is the study of how fast a CPF can decrease as the distance grows. For many spaces, f can be made exponentially decreasing even if we restrict attention to the symmetric case where g=h.
Mar 22, 2017
We have initiated the study of distance-sensitive hashing, an asym- metric class of hashing methods that considerably extend the ca- pabilities of standard LSH.
People also ask
What is LSH used for?
What is the purpose of LSH?
How does Tlsh work?
What is the LSH process?
In this paper we initiate the study of distance-sensitive hashing (DSH), a generalization of LSH that seeks a family of hash functions such that the probability ...
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same buckets with high probability.
Abstract. A Bloom filter is a space-efficient data structure that answers set membership queries with some chance of a false positive.
Jul 5, 2019 · Locality-sensitive hashing (LSH) is one method used to estimate the likelihood of two sequences to have a proper alignment. Using an LSH, it is ...
Locality-sensitive hashing (LSH) is an important tool for managing high-dimensional noisy or uncertain data, for example in connection with data cleaning.
Locality Sensitive Hashing (LSH) refers to a set of algorithmic techniques used to speed up the search for neighbours or duplicate data in the samples.
This paper begins the study of distance-sensitive hashing (DSH), a generalization of LSH that seeks a family of hash functions such that the probability of ...
If points are distance < a/2, then there is at least ½ chance they share a bucket. ◇Yields a (a/2, 2a, 1/2, 1/3)-sensitive family of hash functions. Page ...