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论文类型：期刊论文

发表时间：2016-12-19

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：218

页面范围：120-130

ISSN号：0925-2312

关键字：Dictionary learning; Beta process; Bayesian inference; Gibbs-sampling

摘要：Nonparametric Bayesian techniques have been applied to one dimensional dictionary learning using beta process for sparse representation. However, in real world, signals are often high dimensional tensor and have some structured features. In this paper, we extend the nonparametric Bayesian technique to structured tensor dictionary learning under a sparse favouring beta process prior. The hierarchical form of tensor dictionary learning model was presented, and the inference process was given via Gibbs sampling analysis with analytic update equations. The tensor dictionary is learned directly from high dimensional tensor data, so it can make full use of spatial structure information of the original sample data. The employed nonparametric Bayesian technique allows the noise variance to be unknown or non stationary, the cases frequently being seen in many applications. Finally, several experiments on video reconstruction and image denoising are conducted to showcase the application of learned tensor dictionaries. (C) 2016 Elsevier B.V. All rights reserved.