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Kernel functions based on triplet comparisons

lib:42108e27ec30b4f9 (v1.0.0)

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.

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