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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 571–580 of 796 papers

TitleStatusHype
Optimal Algorithms for Latent Bandits with Cluster Structure—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
Optimal Transport with Heterogeneously Missing Data—0
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy—0
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Orthogonal Inductive Matrix Completion—0
Top-N Recommender System via Matrix Completion—0
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework—0
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