MARKOV CHAIN MONTE CARLO INFERENCE FOR PROBABILISTIC LATENT TENSOR FACTORIZATION

被引：0
|
作者：
Simsekli, Umut [1 ]
Cemgil, A. Taylan [1 ]
机构：
[1] Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey
来源：
2012 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2012年
关键词：
Probabilistic Latent Tensor Factorization (PLTF); Markov Chain Monte Carlo (MCMC); Space Alternating Data Augmentation (SADA);
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Probabilistic Latent Tensor Factorization (PLTF) is a recently proposed probabilistic framework for modeling multiway 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.
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页数：6
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