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Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2075329744

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

About this item

Full title

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2017-01

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

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...

Alternative Titles

Full title

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2075329744

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2075329744

Other Identifiers

E-ISSN

2331-8422

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