Kernel Functions Based on Triplet Comparisons
Abstract
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.
Cite
Text
Kleindessner and von Luxburg. "Kernel Functions Based on Triplet Comparisons." Neural Information Processing Systems, 2017.Markdown
[Kleindessner and von Luxburg. "Kernel Functions Based on Triplet Comparisons." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/kleindessner2017neurips-kernel/)BibTeX
@inproceedings{kleindessner2017neurips-kernel,
title = {{Kernel Functions Based on Triplet Comparisons}},
author = {Kleindessner, Matthäus and von Luxburg, Ulrike},
booktitle = {Neural Information Processing Systems},
year = {2017},
pages = {6807-6817},
url = {https://mlanthology.org/neurips/2017/kleindessner2017neurips-kernel/}
}