A Guide to Similarity Measures
Information Retrieval
2024-08-16 v1 Computer Vision and Pattern Recognition
Machine Learning
Abstract
Similarity measures play a central role in various data science application domains for a wide assortment of tasks. This guide describes a comprehensive set of prevalent similarity measures to serve both non-experts and professional. Non-experts that wish to understand the motivation for a measure as well as how to use it may find a friendly and detailed exposition of the formulas of the measures, whereas experts may find a glance to the principles of designing similarity measures and ideas for a better way to measure similarity for their desired task in a given application domain.
Cite
@article{arxiv.2408.07706,
title = {A Guide to Similarity Measures},
author = {Avivit Levy and B. Riva Shalom and Michal Chalamish},
journal= {arXiv preprint arXiv:2408.07706},
year = {2024}
}
Comments
27 pages