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Representative Selection in Nonmetric Datasets.
Elad Liebman, Benny Chor, and Peter Stone.
"Applied
Artificial Intelligence", 29:807–838, 2015.
This study considers the problem of representative selection: choosing a subset of data points froma dataset that best represents its overall set of elements. This subset needs to inherently reflect thetype of information contained in the entire set, while minimizing redundancy. For such purposes,clustering might seem like a natural approach. However, existing clustering methods are not ideallysuited for representative selection, especially when dealing with nonmetric data, in which only apairwise similarity measure exists. In this article we propose $\delta$-medoids, a novel approach that can beviewed as an extension of the k-medoids algorithm and is specifically suited for sample representativeselection from nonmetric data. We empirically validate δ-medoids in two domains: music analysisand motion analysis. We also show some theoretical bounds on the performance of δ-medoids andthe hardness of representative selection in general.
@article{AAI2015-eladlieb,
author = {Elad Liebman and Benny Chor and Peter Stone},
title = {Representative Selection in Nonmetric Datasets},
Journal= {"Applied Artificial Intelligence"},
Volume="29",
Issue="8",
Year="2015",
pages="807--838",
abstract = {
This study considers the problem of representative selection: choosing a subset of data points from
a dataset that best represents its overall set of elements. This subset needs to inherently reflect the
type of information contained in the entire set, while minimizing redundancy. For such purposes,
clustering might seem like a natural approach. However, existing clustering methods are not ideally
suited for representative selection, especially when dealing with nonmetric data, in which only a
pairwise similarity measure exists. In this article we propose $\delta$-medoids, a novel approach that can be
viewed as an extension of the k-medoids algorithm and is specifically suited for sample representative
selection from nonmetric data. We empirically validate δ-medoids in two domains: music analysis
and motion analysis. We also show some theoretical bounds on the performance of δ-medoids and
the hardness of representative selection in general.
},
}
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