# A Guide to Similarity Measures

arXiv: https://arxiv.org/abs/2408.07706 · HTML: https://mlsanity.com/paper/2408.07706/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 101 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [MIC: Mining Interclass Characteristics for Improved Metric Learning](https://mlsanity.com/paper/1909.11574/) (2019)
- [InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation](https://mlsanity.com/paper/2112.01589/) (2021)
- [Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity](https://mlsanity.com/paper/2107.01396/) (2021)
- [Dimensionality Invariant Similarity Measure](https://mlsanity.com/paper/1409.0923/) (2014)
- [Manipulating Node Similarity Measures in Networks](https://mlsanity.com/paper/1910.11529/) (2019)
- [Small space and streaming pattern matching with k edits](https://mlsanity.com/paper/2106.06037/) (2021)
- [The Dynamic k-Mismatch Problem](https://mlsanity.com/paper/2105.06166/) (2021)

## Built on by (most cited first, 4 of 4)

- [Diversity-Rewarded CFG Distillation](https://mlsanity.com/paper/2410.06084/) (2024)
- [Adaptable Embeddings Network (AEN)](https://mlsanity.com/paper/2411.13786/) (2024)
- [A Compound Classification System Based on Fuzzy Relations Applied to the Noise-Tolerant Control of a Bionic Hand via EMG Signal Recognition](https://mlsanity.com/paper/2509.20523/) (2025)
- [Anisotropy Decides Cosine vs. Rank Metrics for Text Embeddings](https://mlsanity.com/paper/2606.29571/) (2026)

## Abstract (arXiv)

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.