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231224s2015 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2014.2321387
|2 doi
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|a eng
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| 100 |
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|a Gershman, Samuel J
|e verfasserin
|4 aut
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| 245 |
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|a Distance Dependent Infinite Latent Feature Models
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|c 2015
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 24.11.2015
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|a Date Revised 10.09.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Latent 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
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Frazier, Peter I
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Blei, David M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 37(2015), 2 vom: 01. Feb., Seite 334-45
|w (DE-627)NLM098212257
|x 1939-3539
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|g year:2015
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|g month:02
|g pages:334-45
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|u http://dx.doi.org/10.1109/TPAMI.2014.2321387
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