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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 551–600 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
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
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
Online Optimization for Large-Scale Max-Norm Regularization—0
Online Optimization for Max-Norm Regularization—0
Online Policy Learning and Inference by Matrix Completion—0
Online Variational Bayesian Subspace Filtering with Applications—0
On Tensor Completion via Nuclear Norm Minimization—0
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems—0
On the convex geometry of blind deconvolution and matrix completion—0
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs—0
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure—0
Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?—0
On the Power of Adaptivity in Matrix Completion and Approximation—0
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems—0
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