# Kernel functions based on triplet comparisons

arXiv: https://arxiv.org/abs/1607.08456 · HTML: https://mlsanity.com/paper/1607.08456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [A Revenue Function for Comparison-Based Hierarchical Clustering](https://mlsanity.com/paper/2211.16459/) (2022)

## Abstract (arXiv)

Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similarity triplets, we aim at defining kernel functions that correspond to high-dimensional embeddings. These kernel functions can subsequently be used to apply any kernel method to the data set.