Robust clustering tools based on optimal transportation-Reference-Cited by-同舟云学术

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Robust clustering tools based on optimal transportation

Published:2018-01-12 Issue:1 Volume:29 Page:139-160
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

del Barrio E.,Cuesta-Albertos J. A.,Matrán C.^ORCID,Mayo-Íscar A.

Funder

Secretaría de Estado de Investigacion, Desarrollo e Innovacion (ES) and FEDER

Secretaría de Estado de Investigación, Desarrollo e Innovación

Consejería de Educación, Junta de Castilla y León

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

http://link.springer.com/article/10.1007/s11222-018-9800-z/fulltext.html

Reference44 articles.

1. Agueh, M., Carlier, G.: Barycenters in the Wasserstein space. SIAM J. Math. Anal. 43(2), 904–924 (2011)

2. Anderes, E., Borgwardt, S., Miller, J.: Discrete wasserstein barycenters: optimal transport for discrete data. Math. Methods Oper. Res. 84, 389–409 (2016)

3. Álvarez-Esteban, P.C., del Barrio, E., Cuesta-Albertos, J.A., Matrán, C.: A fixed-point approach to barycenters in Wasserstein space. J. Math. Anal. Appl. 441(2), 744–762 (2016)

4. Álvarez-Esteban, P.C., del Barrio, E., Cuesta-Albertos, J.A., Matrán, C.: Wide Consensus aggregation in the Wasserstein Space. Application to location-scatter families. Bernoulli (2017) (to appear)

5. Benamou, J.D., Carlier, G., Cuturi, M., Nenna, L., Peyre, G.: Iterative Bregman projections for regularized transportation problems. SIAM J. Sci. Comput. 37(2), 1111–1138 (2015)

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