NeurIPS 2017poster45 citations
Kernel functions based on triplet comparisons
Matthäus Kleindessner, Ulrike von Luxburg
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.
BibTeX
@inproceedings{NIPS2017_07211688,
author = {Kleindessner, Matth\"{a}us and von Luxburg, Ulrike},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Kernel functions based on triplet comparisons},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/07211688a0869d995947a8fb11b215d6-Paper.pdf},
volume = {30},
year = {2017}
}