On the Application of Convex Transforms to Metric Search

J 2020

On the Application of Convex Transforms to Metric Search

CONNOR, Richard, Alan DEARLE, Vladimír MÍČ a Pavel ZEZULA

Základní údaje

Originální název

On the Application of Convex Transforms to Metric Search

Autoři

CONNOR, Richard (826 Velká Británie a Severní Irsko), Alan DEARLE (826 Velká Británie a Severní Irsko), Vladimír MÍČ (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Pattern Recognition Letters, 2020, 0167-8655

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.756

Kód RIV

RIV/00216224:14330/20:00116150

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1016/j.patrec.2020.08.008

UT WoS

000579804900076

Klíčová slova anglicky

similarity search; transformation of distance function; metric space; convex transform

Štítky

best, DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 5. 2021 05:51, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

CONNOR, Richard, Alan DEARLE, Vladimír MÍČ a Pavel ZEZULA. On the Application of Convex Transforms to Metric Search. Pattern Recognition Letters. 2020, roč. 138, October 2020, s. 563-570. ISSN 0167-8655. Dostupné z: https://dx.doi.org/10.1016/j.patrec.2020.08.008.

@article{1673423,
   author = {Connor, Richard and Dearle, Alan and Míč, Vladimír and Zezula, Pavel},
   article_number = {October 2020},
   doi = {http://dx.doi.org/10.1016/j.patrec.2020.08.008},
   keywords = {similarity search; transformation of distance function; metric space; convex transform},
   language = {eng},
   issn = {0167-8655},
   journal = {Pattern Recognition Letters},
   title = {On the Application of Convex Transforms to Metric Search},
   url = {https://www.sciencedirect.com/science/article/abs/pii/S0167865520303068},
   volume = {138},
   year = {2020}
}

TY  - JOUR
ID  - 1673423
AU  - Connor, Richard - Dearle, Alan - Míč, Vladimír - Zezula, Pavel
PY  - 2020
TI  - On the Application of Convex Transforms to Metric Search
JF  - Pattern Recognition Letters
VL  - 138
IS  - October 2020
SP  - 563-570
EP  - 563-570
SN  - 01678655
KW  - similarity search
KW  - transformation of distance function
KW  - metric space
KW  - convex transform
UR  - https://www.sciencedirect.com/science/article/abs/pii/S0167865520303068
L2  - https://www.sciencedirect.com/science/article/abs/pii/S0167865520303068
N2  - 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.
ER  -

CONNOR, Richard, Alan DEARLE, Vladimír MÍČ a Pavel ZEZULA. On the Application of Convex Transforms to Metric Search. \textit{Pattern Recognition Letters}. 2020, roč.~138, October 2020, s.~563-570. ISSN~0167-8655. Dostupné z: https://dx.doi.org/10.1016/j.patrec.2020.08.008.

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