# Dimensionality Invariant Similarity Measure

arXiv: https://arxiv.org/abs/1409.0923 · HTML: https://mlsanity.com/paper/1409.0923/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2014.

## Built on by (most cited first, 5 of 5)

- [Fidelity and Privacy of Synthetic Medical Data](https://mlsanity.com/paper/2101.08658/) (2021)
- [Stop Oversampling for Class Imbalance Learning: A Critical Review](https://mlsanity.com/paper/2202.03579/) (2022)
- [A Guide to Similarity Measures](https://mlsanity.com/paper/2408.07706/) (2024)
- [KNN and K-means in Gini Prametric Spaces](https://mlsanity.com/paper/2501.18028/) (2025)
- [EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program](https://mlsanity.com/paper/2610.04758/) (2026)

## Abstract (arXiv)

This paper presents a new similarity measure to be used for general tasks including supervised learning, which is represented by the K-nearest neighbor classifier (KNN). The proposed similarity measure is invariant to large differences in some dimensions in the feature space. The proposed metric is proved mathematically to be a metric. To test its viability for different applications, the KNN used the proposed metric for classifying test examples chosen from a number of real datasets. Compared to some other well known metrics, the experimental results show that the proposed metric is a promising distance measure for the KNN classifier with strong potential for a wide range of applications.