Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior-Reference-Cited by-同舟云学术

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Author:

Cheng Lei¹^ORCID,Chen Zhongtao²,Shi Qingjiang³^ORCID,Wu Yik-Chung²^ORCID,Theodoridis Sergios⁴^ORCID

Affiliation:

1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong

3. School of Software Engineering, Tongji University, Shanghai, China

4. National and Kapodistrian University of Athens, Athens, Greece

Funder

National Natural Science Foundation of China

Science and Technology on Sonar Laboratory

National Key Research and Development Project

General Research Fund

Hong Kong Research Grant Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

Reference71 articles.

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2. Global analytic solution of fully-observed variational bayesian matrix factorization;nakajima;J Mach Learn Res,2013

4. Scalable bayesian low-rank decomposition of incomplete multiway tensors;rai;Proc 31st Int Conf Mach Learn,0

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