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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 776–796 of 796 papers

TitleStatusHype
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements—0
A Denoising View of Matrix Completion—0
Penalty Decomposition Methods for Rank Minimization—0
Online Robust Subspace Tracking from Partial InformationCode0
Distributed Matrix Completion and Robust Factorization—0
Sparse Bayesian Methods for Low-Rank Matrix Estimation—0
Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Large-Scale Matrix Factorization with Missing Data under Additional Constraints—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Transduction with Matrix Completion: Three Birds with One Stone—0
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development—0
Link Discovery using Graph Feature Tracking—0
Nuclear norm penalization and optimal rates for noisy low rank matrix completion—0
Robust PCA via Outlier PursuitCode0
Online Identification and Tracking of Subspaces from Highly Incomplete InformationCode0
Matrix Completion from Power-Law Distributed Samples—0
A Gradient Descent Algorithm on the Grassman Manifold for Matrix CompletionCode0
Guaranteed Rank Minimization via Singular Value ProjectionCode0
Matrix Completion from Noisy EntriesCode0
Matrix Completion from a Few EntriesCode0
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