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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 381–390 of 796 papers

TitleStatusHype
Statistical Inferences of Linear Forms for Noisy Matrix Completion—0
Scalable Probabilistic Matrix Factorization with Graph-Based PriorsCode0
Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs: Exact and Stable Recovery—0
Scalable Bayesian Non-linear Matrix Completion—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
Low-rank matrix completion and denoising under Poisson noise—0
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk—0
A divide-and-conquer algorithm for binary matrix completion—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
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