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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 701–750 of 796 papers

TitleStatusHype
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Sequential Matrix Completion—0
A new accelerated gradient method inspired by continuous-time perspective—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
A New Theory for Matrix Completion—0
Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements—0
Low-rank matrix completion theory via Plucker coordinates—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Short-Term Traffic Forecasting Using High-Resolution Traffic Data—0
Signal Recovery on Graphs: Variation Minimization—0
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
A Note on Alternating Minimization Algorithm for the Matrix Completion Problem—0
A note on the statistical view of matrix completion—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A Novel Two-Step Method for Cross Language Representation Learning—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Approximate matrix completion based on cavity method—0
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Approximating Concavely Parameterized Optimization Problems—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
A Riemannian gossip approach to decentralized matrix completion—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
A two-dimensional decomposition approach for matrix completion through gossip—0
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation—0
A Unified Convex Surrogate for the Schatten-p Norm—0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
A Unified Framework for Structured Low-rank Matrix Learning—0
Autoencoder-based Graph Construction for Semi-supervised Learning—0
Multi-target prediction for dummies using two-branch neural networks—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Background Subtraction via Fast Robust Matrix Completion—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Smooth PARAFAC Decomposition for Tensor Completion—0
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