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The Kernel Beta Process
Lu Ren · Yingjian Wang · David B Dunson · Lawrence Carin

Tue Dec 13 04:00 AM -- 04:04 AM (PST) @

A new Le ́vy process prior is proposed for an uncountable collection of covariate- dependent feature-learning measures; the model is called the kernel beta process (KBP). Available covariates are handled efficiently via the kernel construction, with covariates assumed observed with each data sample (“customer”), and latent covariates learned for each feature (“dish”). Each customer selects dishes from an infinite buffet, in a manner analogous to the beta process, with the added constraint that a customer first decides probabilistically whether to “consider” a dish, based on the distance in covariate space between the customer and dish. If a customer does consider a particular dish, that dish is then selected probabilistically as in the beta process. The beta process is recovered as a limiting case of the KBP. An efficient Gibbs sampler is developed for computations, and state-of-the-art results are presented for image processing and music analysis tasks.

Author Information

Lu Ren (Duke University)
Yingjian Wang (Duke University)
David B Dunson (Duke University)
Lawrence Carin (KAUST)

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