Poster
Kernel functions based on triplet comparisons
Matthäus Kleindessner · Ulrike von Luxburg
Pacific Ballroom #41
Keywords: [ Ranking and Preference Learning ] [ Similarity and Distance Learning ]
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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