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Matrix Completion Based on Low-Rank and Local Features Applied to Images Recovery and Recommendation Systems

Recent advances have shown that the challenging problem of matrix completion arises from real-world applications, such as image recovery, and recommendation systems. Existing matrix completion methods utilize the low-rank property of sparse matrices to fill missing entries, which essentially exploit...

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Principais autores: Ying Zhang, Kai Xu, Songfeng Liang, Chen Zhao
Formato: Artigo
Idioma:Inglês
Publicado em: IEEE 2022-01-01
coleção:IEEE Access
Assuntos:
Matrix completion
image completion
low-rank matrix
image recovery
recommendation systems
Acesso em linha:https://ieeexplore.ieee.org/document/9878118/
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