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Transformer Modifications
Published in Uday Kamath, Kenneth L. Graham, Wael Emara, Transformers for Machine Learning, 2022
Uday Kamath, Kenneth L. Graham, Wael Emara
Locality-sensitive hashing, or LSH, was introduced in 1998, in [129] as a method of approximate similarity search based on hashing. In formal terms, LSH is based on a family of hash functions F that operate on a collection of items where, if any two such items x and y, Probh∈F[h(x)=h(y)]=sim(x,y), where sim(x,y)∈[0,1] in a similarity function defined on the collection of items [39]. Or, in other words, you have an LSH method whenever you can hash the items in a dataset such that the collision probability of any two items is much higher when those items are close together than when they're far apart.
O(1) Search by Hashing
Published in Suman Saha, Shailendra Shukla, Advanced Data Structures, 2019
[myfirstindex]Locality Sensitive Hashing Locality sensitive hashing (LSH) is a data structure to perform probabilistic dimension reduction of high-dimensional data and efficiently handle similarity searches, thus defeating the “curse of dimensionality.” The basic idea is to hash the input items so that similar items are mapped to the same buckets with high probability (the number of buckets being much smaller than the universe of possible input items).
BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs
Published in IETE Journal of Research, 2023
G. Sharmila, M. K. Kavitha Devi
Chae et al. [23] propose a new concept called graph bag, which is comprised of web pages, XML documents, images and drugs. Dynamic bag classification is implemented in this paper and a very informative feature set (top feature set) is considered to deal with the stream of bags. Finally it classifies the dynamic bags. Feature selection at each time point increases the time complexity. In [24] a similarity search is implemented for large graph databases by a fast algorithm. The problem of K-NN (K-Nearest Neighbor)-based similarity search is concentrated and Locality Sensitive Hashing (LSH) is proposed. For each query, Euclidean distance space is invoked with the graph database. It is conducted for image retrieval and images are considered to be attributed to the graph. There are two challenges that are unfilled in this paper, namely, vector-based graph representation consumes larger time and the LSH method requires high storage space.
Big Data Retrieval Using Locality-Sensitive Hashing with Document-Based NoSQL Database
Published in IETE Journal of Research, 2021
N.R. Gayathiri, A.M. Natarajan
Locality-sensitive hashing (LSH) belongs to the randomized approach which supersedes the neighbor search difficulty in the high-dimensional spaces. Randomized set of rules provides an excessive possibility in order to guarantee that it is going to return the right answer or one near it instead does not assure an actual solution [4]. By making an investment additional computational attempt, the opportunity may be driven as high as desired. The LSH is based totally on the easy reality that, if two data points are near collectively, then after sporting out a “projection” operation these information factors will remain near together [4]. The LSH makes use of hash capabilities which makes comparable objects having excessive probabilities located within the same hash buckets, wherein multiple items may be positioned in distinctive buckets with excessive likelihoods [5].