Bayesian Nonnegative Matrix Factorization as Allocation Model

Bayesian Nonnegative Matrix Factorization as Allocation Model

In this project, we provide efficient, parallelized inference algorithms for Allocation Model which has a close relationship with Nonnegative Matrix Factorization. NMF is the problem of writing a nonnegative matrix, X, as the multiplication two nonnegative factor matrices, W and H. Bayesian analysis of NMF models show us that (M x N) X matrix is implicitly decomposed into a hidden (M x N x K) tensor S. Allocation Model starts from this idea, and analyzes hidden tensor S more explicitly. We demonstrate the advantages of Allocation Model by challenging our inference algorithms on some examples and also on well-known public datasets. All of our algorithms are implemented in C++ and parallelized using OpenMP and CUDA.

Project Poster: 

Project Members: 

Eşref Özdemir

Project Advisor: 

Ali Taylan Cemgil

Project Status: 

Project Year: 

  • Spring

Contact us

Department of Computer Engineering, Boğaziçi University,
34342 Bebek, Istanbul, Turkey

  • Phone: +90 212 359 45 23/24
  • Fax: +90 212 2872461

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