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Poster
Kernel functions based on triplet comparisons
Matthäus Kleindessner · Ulrike von Luxburg
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.
Author Information
Matthäus Kleindessner (University of Tübingen)
Ulrike von Luxburg (University of Tübingen)
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