Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution....

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Detalles Bibliográficos
Autores principales: Tokuda, Tomoki, Yoshimoto, Junichiro, Shimizu, Yu, Okada, Go, Takamura, Masahiro, Okamoto, Yasumasa, Yamawaki, Shigeto, Doya, Kenji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648298/
https://www.ncbi.nlm.nih.gov/pubmed/29049392
http://dx.doi.org/10.1371/journal.pone.0186566

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648298/
https://www.ncbi.nlm.nih.gov/pubmed/29049392
http://dx.doi.org/10.1371/journal.pone.0186566

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