# Mode estimation for high dimensional discrete tree graphical models

## Author(s): Chen, C; Liu, H; Metaxas, DN; Zhao, T

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1qw17
DC FieldValueLanguage
dc.contributor.authorChen, C-
dc.contributor.authorLiu, H-
dc.contributor.authorMetaxas, DN-
dc.contributor.authorZhao, T-
dc.date.accessioned2021-10-11T14:16:51Z-
dc.date.available2021-10-11T14:16:51Z-
dc.date.issued2014en_US
dc.identifier.citationChen, Chao, Han Liu, Dimitris Metaxas, and Tianqi Zhao. "Mode estimation for high dimensional discrete tree graphical models." Advances in Neural Information Processing Systems 27, (2014): pp. 1323-1331.en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttp://papers.nips.cc/paper/5533-mode-estimation-for-high-dimensional-discrete-tree-graphical-models-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1qw17-
dc.description.abstractThis paper studies the following problem: given samples from a high dimensional discrete distribution, we want to estimate the leading (δ,ρ)-modes of the underlying distributions. A point is defined to be a (δ,ρ)-mode if it is a local optimum of the density within a δ-neighborhood under metric ρ. As we increase the scale'' parameter δ, the neighborhood size increases and the total number of modes monotonically decreases. The sequence of the (δ,ρ)-modes reveal intrinsic topographical information of the underlying distributions. Though the mode finding problem is generally intractable in high dimensions, this paper unveils that, if the distribution can be approximated well by a tree graphical model, mode characterization is significantly easier. An efficient algorithm with provable theoretical guarantees is proposed and is applied to applications like data analysis and multiple predictions.en_US
dc.format.extent1323 - 1331en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systems 27en_US
dc.rightsAuthor's manuscripten_US
dc.titleMode estimation for high dimensional discrete tree graphical modelsen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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