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 131–140 of 796 papers

TitleStatusHype
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Approximating Concavely Parameterized Optimization Problems—0
A Harmonic Extension Approach for Collaborative Ranking—0
Adaptive Multinomial Matrix Completion—0
Completing Any Low-rank Matrix, Provably—0
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Show:102550
← PrevPage 14 of 80Next →

No leaderboard results yet.