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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 551–575 of 796 papers

TitleStatusHype
New Perspectives on k-Support and Cluster Norms—0
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective—0
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling—0
Noisy Inductive Matrix Completion Under Sparse Factor Models—0
Noisy Matrix Completion under Sparse Factor Models—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Noisy Tensor Completion via the Sum-of-Squares Hierarchy—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Non-Convex Matrix Completion Against a Semi-Random Adversary—0
Nonconvex Matrix Completion with Linearly Parameterized Factors—0
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview—0
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks—0
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning—0
Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery—0
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Norm-Bounded Low-Rank Adaptation—0
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis—0
Notes on Low-rank Matrix Factorization—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Nuclear norm penalization and optimal rates for noisy low rank matrix completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
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