LAUSR

Efficient locality-sensitive hashing over high-dimensional streaming data

Sign Up to like & get
recommendations!
0 Published in 2020 at "Neural Computing and Applications"

DOI: 10.1007/s00521-020-05336-1

Abstract: Approximate nearest neighbor (ANN) search in high-dimensional spaces is fundamental in many applications. Locality-sensitive hashing (LSH) is a well-known methodology to solve the ANN problem. Existing LSH-based ANN solutions typically employ a large number of… read more here.

Keywords: streaming data; sensitive hashing; dimensional streaming; high dimensional ... See more keywords

Use of locality sensitive hashing (LSH) algorithm to match Web of Science and Scopus

Sign Up to like & get
recommendations!
1 Published in 2017 at "Scientometrics"

DOI: 10.1007/s11192-017-2569-6

Abstract: A novel hashing algorithm is applied to match two prominent and important bibliographic databases at the paper level. In the literature, such tasks have been studied and conducted many times, but relying only on journal… read more here.

Keywords: locality sensitive; match; sensitive hashing; web science ... See more keywords

Locality sensitive request distribution for fog and cloud servers

Sign Up to like & get
recommendations!
1 Published in 2019 at "Service Oriented Computing and Applications"

DOI: 10.1007/s11761-019-00260-2

Abstract: AbstractFog computing is meant to bring the cloud resource closer to the edge of the Internet so that devices can access the back end services much faster. Additionally, the services hosted at the fogs can… read more here.

Keywords: cloud; locality sensitive; request distribution; fog ... See more keywords

Quantifying object similarity: Applying locality sensitive hashing for comparing material culture

Sign Up to like & get
recommendations!
1 Published in 2020 at "Journal of Archaeological Science"

DOI: 10.1016/j.jas.2020.105257

Abstract: We present a novel technique that compares and quantifies images used here to compare similarities between material cultures. This method is based on locality sensitive hashing (LSH), which uses a relatively fast and flexible algorithm… read more here.

Keywords: sensitive hashing; similarity; locality sensitive; object similarity ... See more keywords

Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype

Sign Up to like & get
recommendations!
1 Published in 2022 at "Journal of Computational Biology"

DOI: 10.1089/cmb.2021.0640

Abstract: Microbial organisms play important roles in many aspects of human health and diseases. Encouraged by the numerous studies that show the association between microbiomes and human diseases, computational and machine learning methods have been recently… read more here.

Keywords: host phenotype; locality sensitive; sensitive hashing; host ... See more keywords

A Binary-Search-Based Locality-Sensitive Hashing Method for Cross-Site User Identification

Sign Up to like & get
recommendations!
2 Published in 2023 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2022.3158664

Abstract: In recent years, we have witnessed increasing attention paid to the problem of cross-site user identification (CSUI) with the bloom of social media. Despite noticeable progress in this field, the problem of enormous computation posed… read more here.

Keywords: user identification; binary search; cross site; site user ... See more keywords

Hamming Metric Multi-Granularity Locality-Sensitive Bloom Filter

Sign Up to like & get
recommendations!
0 Published in 2018 at "IEEE/ACM Transactions on Networking"

DOI: 10.1109/tnet.2018.2850536

Abstract: A Bloom filter is a type of space-efficient data structure that supports membership tests in numerous network applications. Recently, emerging applications require an approximate membership test (AMT) rather than conventional (exact-matching) membership test. Some AMT… read more here.

Keywords: bloom; locality sensitive; bloom filter; hamming metric ... See more keywords

In Defense of Locality-Sensitive Hashing

Sign Up to like & get
recommendations!
0 Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"

DOI: 10.1109/tnnls.2016.2615085

Abstract: Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training… read more here.

Keywords: locality sensitive; locality; sensitive hashing; two step ... See more keywords