OSPA barycenters for clustering set-valued data

Document Type

Conference Proceeding

Publication Date

9-14-2015

Publication Title

2015 18th International Conference on Information Fusion, Fusion 2015

First Page

1375

Keywords

barycenter, Clustering, k-means, OSPA distance, point sets, set-valued data, Wasserstein distance

Last Page

1381

Abstract

We consider the problem of clustering set-valued observations, i.e., each observation is a set that consists of a finite number of real vectors. For this purpose, we develop a k-means algorithm that employs the OSPA distance for measuring the distance between sets. In particular, we introduce a novel alternating optimization algorithm for the OSPA barycenter of sets with varying cardinalities that is required for calculating cluster centroids efficiently. The benefits of clustering set-valued data with respect to the OSPA distance are illustrated by means of simulated experiments in the context of target tracking and recognition.

ISBN

9780982443866

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