SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 321–330 of 796 papers

TitleStatusHype
A note on the statistical view of matrix completion—0
Identifying global optimality for dictionary learning—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays—0
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion—0
Implicit bias of SGD in L_2-regularized linear DNNs: One-way jumps from high to low rank—0
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
Implicit Regularization in Deep Tensor Factorization—0
Fast Dual-Regularized Autoencoder for Sparse Biological Data—0
Show:102550
← PrevPage 33 of 80Next →

No leaderboard results yet.