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Distance Dependent Infinite Latent Feature Models

Author(s): Gershman, Samuel J; Frazier, Peter I; Blei, David M

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dc.contributor.authorGershman, Samuel J-
dc.contributor.authorFrazier, Peter I-
dc.contributor.authorBlei, David M-
dc.date.accessioned2021-10-08T19:49:00Z-
dc.date.available2021-10-08T19:49:00Z-
dc.date.issued2014en_US
dc.identifier.citationGershman, Samuel J., Peter I. Frazier, and David M. Blei. "Distance Dependent Infinite Latent Feature Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 2 (2014): pp. 334-345. doi:10.1109/TPAMI.2014.2321387en_US
dc.identifier.issn0162-8828-
dc.identifier.urihttps://arxiv.org/pdf/1110.5454.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1pz59-
dc.description.abstractLatent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the distance dependent Indian buffet process (dd-IBP), for modeling non-exchangeable data. It relies on distances defined between data points, biasing nearby data to share more features. The choice of distance measure allows for many kinds of dependencies, including temporal and spatial. Further, the original IBP is a special case of the dd-IBP. We develop the dd-IBP and theoretically characterize its feature-sharing properties. We derive a Markov chain Monte Carlo sampler for a linear Gaussian model with a dd-IBP prior and study its performance on real-world non-exchangeable data.en_US
dc.format.extent334 - 345en_US
dc.language.isoen_USen_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.rightsAuthor's manuscripten_US
dc.titleDistance Dependent Infinite Latent Feature Modelsen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1109/TPAMI.2014.2321387-
dc.identifier.eissn1939-3539-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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