Locating similar names through locality sensitive hashing and graph theory

García, Fernando Santa Cecilia; Villalba, Luis Javier García; Orozco, Ana Lucila Sandoval; Ruiz, Francisco Damián Aranda; Juárez, Andrés Aguirre; Kim, Tai-hoon

doi:10.1007/s11042-018-6375-9

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Geoscience uses Voronoi LSH [39] to process hundreds of polygons and points in real-time to improve the scalability of geographic applications and accelerate the construction of roadmaps without compromising quality [50] . LSH-RANSAC [51] addressed the issue of feature-based robot localization in large-size maps. Locality-sensitive hashing can also be applied as an ideal signal classification scheme in raw mass spectrometry data classification in the medical field [52] .LSH-ALL-PAIRS effectively compares genomic DNA sequences intending to discover conserved genomic features across species.…”

Section: Other Recent Advancesmentioning

confidence: 99%

“…A method called semi-supervised SimHash [50] was proposed based on simhash for similarity queries of documents, which learns the optimal feature weights and uses the weights to find query results and for processing large text collections. In addition a method for identifying spelling errors and near duplicate names by LSH [51] . Finding audio similar items after adding wavelet-based LSH [52] for fingerprinting can effectively avoid the traditional k-ANN approach of matching samples from the test and candidate sets and improve efficiency.…”

Section: Other Recent Advancesmentioning

confidence: 99%

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Recent advances in locality-sensitive hashing and its performance in different applications

Yuan

Liu

Luo

et al. 2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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This paper investigates the recent progress of Locality-sensitive hashing (LSH) under different metrics and its adaptive performance in different application scenarios. LSH is an approximate nearest neighbor query algorithm, which aims to perform fast similarity finding in high-dimensional space. In recent years, the improvement directions of LSH can be divided into three categories: 1) constructing suitable function families under different metric indexes, 2) constructing detection sequences by perturbation, and 3) expanding the radius to improve the retrieval range. Since metric indexing is more widely used, this paper focuses on a comprehensive and schematic review of this approach. computational simplicity and efficiency of LSH, which is prominent in the fields of image, recommendation, and de-duplication, we review the latest research on LSH in different application scenarios to provide an advanced general framework and a better understanding of LSH in combination with other fields. In conclusion, various algorithms for LSH are evolving rapidly, and it is expected to see more diverse application scenarios for these methods in the future.

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Section: Other Recent Advancesmentioning

confidence: 99%

Section: Other Recent Advancesmentioning

confidence: 99%

Recent advances in locality-sensitive hashing and its performance in different applications

Yuan

Liu

Luo

et al. 2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

View full text Add to dashboard Cite

show abstract

“…In [37], the paper proposes a method to identify misspelled names and near-duplicates using LSH. First, the data is transformed and similar names (candidates) are produced from the transformed data using LSH with Jaccard distance.…”

Section: Text/document Processingmentioning

confidence: 99%

A Survey on Locality Sensitive Hashing Algorithms and their Applications

Jafari¹,

Maurya²,

Nagarkar³

et al. 2021

Preprint

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Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular techniques for finding approximate nearest neighbor searches in high-dimensional spaces. The main benefits of LSH are its sub-linear query performance and theoretical guarantees on the query accuracy. In this survey paper, we provide a review of state-of-the-art LSH and Distributed LSH techniques. Most importantly, unlike any other prior survey, we present how Locality Sensitive Hashing is utilized in different application domains.CCS Concepts: • General and reference → Surveys and overviews.

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“…LSH has a wide range of applications, including near-duplicate detection (i.e. to deduplicate large quantities of documents and webpages) [239], genome-wide association study (i.e. to identify similar gene expressions in genome databases) [240], large-scale image search (i.e.…”

Section: Distinction Between the Association Table And Other Techniquesmentioning

confidence: 99%

Contributions to Case-Based Reasoning Enabled Decision Support System for Smart Agriculture

Zhai¹

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Locating similar names through locality sensitive hashing and graph theory

Cited by 3 publications

References 17 publications

Recent advances in locality-sensitive hashing and its performance in different applications

Recent advances in locality-sensitive hashing and its performance in different applications

A Survey on Locality Sensitive Hashing Algorithms and their Applications

Contributions to Case-Based Reasoning Enabled Decision Support System for Smart Agriculture

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