Imbalanced low-rank tensor completion via latent matrix factorization

Tensor completion has been widely used in computer vision and machine learning. Most existing tensor completion methods empirically assume the intrinsic tensor is simultaneous low-rank in all over modes. However, tensor data recorded from real-world applications may conflict with these assumptions,...

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Vydáno v:Neural networks Ročník 155; s. 369 - 382
Hlavní autoři: Qiu, Yuning, Zhou, Guoxu, Zeng, Junhua, Zhao, Qibin, Xie, Shengli
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2022
Témata:
Image/video inpainting
Low-rank tensor recovery
Tensor analysis
Tensor completion
Tensor ring decomposition
Tensor completion
Low-rank tensor recovery
Tensor analysis
Image/video inpainting
Tensor ring decomposition
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí:Tensor completion has been widely used in computer vision and machine learning. Most existing tensor completion methods empirically assume the intrinsic tensor is simultaneous low-rank in all over modes. However, tensor data recorded from real-world applications may conflict with these assumptions, e.g., face images taken from different subjects often lie in a union of low-rank subspaces, which may result in a quite high rank or even full rank structure in its sample mode. To this aim, in this paper, we propose an imbalanced low-rank tensor completion method, which can flexibly estimate the low-rank incomplete tensor via decomposing it into a mixture of multiple latent tensor ring (TR) rank components. Specifically, each latent component is approximated using low-rank matrix factorization based on TR unfolding matrix. In addition, an effective proximal alternating minimization algorithm is developed and theoretically proved to maintain the global convergence property, that is, the whole sequence of iterates is convergent and converges to a critical point. Extensive experiments on both synthetic and real-world tensor data demonstrate that the proposed method achieves more favorable completion results with less computational cost when compared to the state-of-the-art tensor completion methods.
Bibliografie:ObjectType-Article-1
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2022.08.023