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Abstract Details

Activity Number: 352
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306657
Title: Infinite Tensor Factorization Priors
Author(s): Anjishnu Banerjee*+ and David Dunson
Companies: and Duke University
Address: 2748 Campus Walk Avenue, Apt 14H, Durham, NC, 27705, United States
Keywords: Data Fusion ; Depedant Cluster ; Dirichlet Process ; PARAFAC ; Tensor Decomposition ; Transfer Learning

There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions, images and documents. We consider a general framework for nonparametric Bayes joint modeling through mixture models that incorporate dependence across data types through a joint mixing measure. The mixing measure is assigned a novel infinite tensor factorization (ITF) prior that allows flexible dependence in cluster allocation across data types. The ITF prior is formulated as a tensor product of stick-breaking processes. Focusing on a convenient special case corresponding to a Parafac factorization, we provide basic theory justifying the flexibility of the proposed prior and resulting asymptotic properties. Focusing on ITF mixtures of product kernels, we develop a Gibbs sampling algorithm for routine implementation relying on slice sampling. The methods are compared with alternative joint mixture models based on Dirichlet processes and related approaches through simulations and real data applications.

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