Files in this item
On the application of convex transforms to metric search
Item metadata
dc.contributor.author | Connor, Richard | |
dc.contributor.author | Dearle, Alan | |
dc.contributor.author | Mic, Vladimir | |
dc.contributor.author | Zezula, Pavel | |
dc.date.accessioned | 2021-08-07T23:39:16Z | |
dc.date.available | 2021-08-07T23:39:16Z | |
dc.date.issued | 2020-10 | |
dc.identifier | 269563545 | |
dc.identifier | e85606ea-0691-4bd0-b3b0-67263af6be1b | |
dc.identifier | 85090410278 | |
dc.identifier | 000579804900076 | |
dc.identifier.citation | Connor , R , Dearle , A , Mic , V & Zezula , P 2020 , ' On the application of convex transforms to metric search ' , Pattern Recognition Letters , vol. 138 , pp. 563-570 . https://doi.org/10.1016/j.patrec.2020.08.008 | en |
dc.identifier.issn | 0167-8655 | |
dc.identifier.other | RIS: urn:998FF4F9C6E235C2A95E835000290168 | |
dc.identifier.uri | https://hdl.handle.net/10023/23737 | |
dc.description | Funding: This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No.CZ.02.1.01/0.0/0.0/16 019/0000822) and by ESRC “Administrative Data Research Centres 2018” (No. ES/S007407/1). | en |
dc.description.abstract | Scalable similarity search in metric spaces relies on using the mathematical properties of the space in order to allow efficient querying. Most important in this context is the triangle inequality property, which can allow the majority of individual similarity comparisons to be avoided for a given query. However many important metric spaces, typically those with high dimensionality, are not amenable to such techniques. In the past convex transforms have been studied as a pragmatic mechanism which can overcome this effect; however the problem with this approach is that the metric properties may be lost, leading to loss of accuracy. Here, we study the underlying properties of such transforms and their effect on metric indexing mechanisms. We show there are some spaces where certain transforms may be applied without loss of accuracy, and further spaces where we can understand the engineering tradeoffs between accuracy and efficiency. We back these observations with experimental analysis. To highlight the value of the approach, we show three large spaces deriving from practical domains whose dimensionality prevents normal indexing techniques, but where the transforms applied give scalable access with a relatively small loss of accuracy. | |
dc.format.extent | 777255 | |
dc.language.iso | eng | |
dc.relation.ispartof | Pattern Recognition Letters | en |
dc.subject | Metric search | en |
dc.subject | Contex transform | en |
dc.subject | Metric space | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | DAS | en |
dc.subject | BDC | en |
dc.subject | R2C | en |
dc.subject | ~DC~ | en |
dc.subject.lcc | QA75 | en |
dc.title | On the application of convex transforms to metric search | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.identifier.doi | 10.1016/j.patrec.2020.08.008 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2021-08-08 |
This item appears in the following Collection(s)
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.