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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 431–440 of 796 papers

TitleStatusHype
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers—0
Large-Scale Matrix Factorization with Missing Data under Additional Constraints—0
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion—0
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development—0
Learning Counterfactual Distributions via Kernel Nearest Neighbors—0
Learning from Ambiguously Labeled Face Images—0
Learning Iterative Reasoning through Energy Diffusion—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions—0
Learning of Generalized Low-Rank Models: A Greedy Approach—0
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