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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 501–550 of 796 papers

TitleStatusHype
Negative Binomial Matrix Completion—0
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations—0
Network Inference by Learned Node-Specific Degree Prior—0
New Hardness Results for Low-Rank Matrix Completion—0
New Perspectives on k-Support and Cluster Norms—0
New Perspectives on k-Support and Cluster Norms—0
The heterogeneous impact of the EU-Canada agreement with causal machine learning—0
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective—0
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling—0
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk—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
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis—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
The Noisy Power Method: A Meta Algorithm with Applications—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
The radius of statistical efficiency—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Ocean Reverberation Suppression via Matrix Completion with Sensor Failure—0
On adaptivity and minimax optimality of two-sided nearest neighbors—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
One-Bit Matrix Completion with Differential Privacy—0
One-sided Matrix Completion from Two Observations Per Row—0
Online Algorithms for Factorization-Based Structure from Motion—0
Online high rank matrix completion—0
The Singular Value Decomposition, Applications and Beyond—0
Online Low Rank Matrix Completion—0
Online Matrix Completion: A Collaborative Approach with Hott Items—0
Online Matrix Completion and Online Robust PCA—0
Online Matrix Completion Through Nuclear Norm Regularisation—0
Online Matrix Completion with Side Information—0
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