Fast Discrete Distribution Clustering Using Wasserstein Barycenter With Sparse Support

@article{Ye2017FastDD,
  title={Fast Discrete Distribution Clustering Using Wasserstein Barycenter With Sparse Support},
  author={Jianbo Ye and P. Wu and J. Z. Wang and Jia Li},
  journal={IEEE Transactions on Signal Processing},
  year={2017},
  volume={65},
  pages={2317-2332}
}
  • Jianbo Ye, P. Wu, +1 author Jia Li
  • Published 2017
  • Computer Science, Mathematics, Psychology
  • IEEE Transactions on Signal Processing
  • In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich–Wasserstein metric. D2-clustering has a severe scalability issue, the bottleneck being the computation of a centroid distribution, called Wasserstein barycenter, that minimizes its sum of squared… CONTINUE READING
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