Dynamic clustering via asymptotics of the dependent Dirichlet process mixture

Dynamic clustering via asymptotics of the dependent Dirichlet process mixture

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDP...

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Bibliographic Details
Main Authors: Campbell, Trevor David, Liu, Miao, Kulis, Brian, How, Jonathan P., Carin, Lawrence
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Language:en_US
Published: Neural Information Processing Systems Foundation 2015
Online Access:http://hdl.handle.net/1721.1/96963
https://orcid.org/0000-0003-1499-0191
https://orcid.org/0000-0001-8576-1930

Internet

http://hdl.handle.net/1721.1/96963
https://orcid.org/0000-0003-1499-0191
https://orcid.org/0000-0001-8576-1930

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