期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 66, 期 11, 页码 2804-2817出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2018.2816575
关键词
Matrix completion; low-rank Bayesian learning; generalized approximate massage passing
资金
- National Science Foundation of China [61522104, U1530154]
The problem of low-rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rankmatrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and aWishart distribution is specified as a hyperprior over the precision matrix. We show that such a hierarchical Gaussian prior has the potential to encourage a low-rank solution. Based on the proposed hierarchical prior model, we develop a variational Bayesian matrix completion method, which embeds the generalized approximate massage passing technique to circumvent cumbersome matrix inverse operations. Simulation results show that our proposedmethod demonstrates superiority over some state-of the-art matrix completion methods.
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