Improving Hamming-Distance Computation for Adaptive Similarity Search Approach

Vikram Singh and Chandradeep Kumar
Additional contact information
Chandradeep Kumar: National Institute of Technology, Kurukshetra, India

International Journal of Intelligent Information Technologies (IJIIT), 2022, vol. 18, issue 2, 1-17

Abstract: In the modern context, the similarity is determined by content-preserving stimuli, retrieval of relevant ‘nearest neighbor' objects, and the way similar objects are pursued. Current similarity search in hamming-space-based strategies finds all the data objects within a threshold hamming-distance for a user query, though the number of computations for distance and candidate generation are key concerns from the many years. The hamming-space paradigm extends the range of alternatives for an optimized search experience. A novel counting-based similarity search strategy is proposed with an improved hamming-space (e.g., optimized candidate generation and verification function). The strategy adapts towards the lesser set of user query dimensions and subsequently constrains the hamming-space computations with each data object driven by generated statistics. The extensive evaluation asserts that the proposed counting-based approach can be combined with any pigeonhole principle-based similarity search to further improve its performance.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.296270 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:18:y:2022:i:2:p:1-17

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().