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@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 and Pavel ZEZULA. On the Application of Convex Transforms to Metric Search. \textit{Pattern Recognition Letters}. 2020, vol.~138, October 2020, p.~563-570. ISSN~0167-8655. Available from: https://dx.doi.org/10.1016/j.patrec.2020.08.008.
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