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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

Showing 201–250 of 796 papers

TitleStatusHype
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs—0
Deterministic Symmetric Positive Semidefinite Matrix Completion—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Differentially Private Matrix Completion Revisited—0
Discovering Abstract Symbolic Relations by Learning Unitary Group Representations—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Discrete-Aware Matrix Completion via Proximal Gradient—0
Distributed Matrix Completion and Robust Factorization—0
Distributed Representations for Building Profiles of Users and Items from Text Reviews—0
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Doubly Robust Inference in Causal Latent Factor Models—0
Doubly robust nearest neighbors in factor models—0
Dropout: Explicit Forms and Capacity Control—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
Efficient Federated Low Rank Matrix Completion—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
Efficient Low Rank Tensor Ring Completion—0
Efficiently escaping saddle points on manifolds—0
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries—0
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra—0
Bounded Manifold Completion—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Efficient Rigid Body Localization based on Euclidean Distance Matrix Completion for AGV Positioning under Harsh Environment—0
Empirical Bayes Matrix Completion—0
Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion—0
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem—0
Ensemble Methods for Causal Effects in Panel Data Settings—0
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows—0
Errata: Distant Supervision for Relation Extraction with Matrix Completion—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Estimation of Missing Data in Intelligent Transportation System—0
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent—0
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
A Novel Two-Step Method for Cross Language Representation Learning—0
Exact tensor completion with sum-of-squares—0
Exploiting Observation Bias to Improve Matrix Completion—0
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Exponential Family Matrix Completion under Structural Constraints—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
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