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 751–775 of 796 papers

TitleStatusHype
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Flexible Low-Rank Statistical Modeling with Side Information—0
On the Predictability of Human Assessment: when Matrix Completion Meets NLP Evaluation—0
Completing Any Low-rank Matrix, Provably—0
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Provable Inductive Matrix Completion—0
Robust Spectral Compressed Sensing via Structured Matrix Completion—0
Low-rank optimization for distance matrix completion—0
Spectral Compressed Sensing via Structured Matrix Completion—0
Matrix Completion via Max-Norm Constrained Optimization—0
Missing Entries Matrix Approximation and Completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
Approximating Concavely Parameterized Optimization Problems—0
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning—0
Calibrated Elastic Regularization in Matrix Completion—0
Relax and Randomize : From Value to Algorithms—0
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
Low-rank optimization with trace norm penalty—0
RTRMC: A Riemannian trust-region method for low-rank matrix completion—0
Show:102550
← PrevPage 31 of 32Next →

No leaderboard results yet.