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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 441–450 of 796 papers

TitleStatusHype
Matrix Completion via Residual Spectral Matching—0
Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms—0
Matrix completion with deterministic pattern - a geometric perspective—0
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach—0
Matrix Completion with Heterogonous Cost—0
Matrix Completion with Hierarchical Graph Side Information—0
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms—0
Matrix Completion with Model-free Weighting—0
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