Authors: Matthäus Kleindessner,Ulrike von Luxburg
Where published:
NeurIPS 2017 12
ArXiv: 1607.08456
Document:
PDF
DOI
Abstract URL: http://arxiv.org/abs/1607.08456v2
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.