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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 211–220 of 796 papers

TitleStatusHype
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Doubly Robust Inference in Causal Latent Factor Models—0
Doubly robust nearest neighbors in factor models—0
Dropout: Explicit Forms and Capacity Control—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
A Novel Two-Step Method for Cross Language Representation Learning—0
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