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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 491–500 of 796 papers

TitleStatusHype
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form—0
Sequence-Aware Recommender SystemsCode0
Structured low-rank matrix completion for forecasting in time series analysis—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
Active Feature Acquisition with Supervised Matrix Completion—0
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion—0
Matrix completion with deterministic pattern - a geometric perspective—0
NGS Based Haplotype Assembly Using Matrix CompletionCode0
Matrix Completion for Structured Observations—0
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