Variational Bayesian Inference for CP Tensor Completion with Subspace Information

被引：0
|
作者：
Budzinskiy, S. [1 ]
Zamarashkin, N. [1 ]
机构：
[1] Russian Acad Sci, Marchuk Inst Numer Math, Moscow 119333, Russia
来源：
LOBACHEVSKII JOURNAL OF MATHEMATICS | 2023年 / 44卷 / 08期
基金：
俄罗斯科学基金会;
关键词：
tensor completion; CP decomposition; PARAFAC; Bayesian inference; subspace information; RANK; DECOMPOSITION; FACTORIZATION; PARAFAC;
D O I：
10.1134/S1995080223080103
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
We propose an algorithm for Bayesian low-rank tensor completion with automatic rank determination in the canonical polyadic format when additional subspace information (SI) is given. We numerically validate the regularization properties induced by SI and present the results about tensor recovery and rank determination. The results show that the number of samples required for successful completion is significantly reduced in the presence of SI.
引用
收藏
页码：3016 / 3027
页数：12
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