Markov Chain Monte Carlo inference for probabilistic latent tensor factorization

Abstract

Probabilistic Latent Tensor Factorization (PLTF) is a recently proposed probabilistic framework for modeling multi-way data. Not only the popular tensor factorization models but also any arbitrary tensor factorization structure can be realized by the PLTF framework. This paper presents Markov Chain Monte Carlo procedures (namely the Gibbs sampler) for making inference on the PLTF framework. We provide the abstract algorithms that are derived for the general case and the overall procedure is illustrated on both synthetic and real data.