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...

Celý popis

Podrobná bibliografie

Hlavní autoři:	Ying Zhang, Kai Xu, Songfeng Liang, Chen Zhao
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2022-01-01
Edice:	IEEE Access
Témata:	Matrix completion image completion low-rank matrix image recovery recommendation systems
On-line přístup:	https://ieeexplore.ieee.org/document/9878118/
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!