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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
Fast Exact Matrix Completion with Finite Samples—0
CUR Algorithm for Partially Observed Matrices—0
Noisy Matrix Completion under Sparse Factor Models—0
Generalized Conditional Gradient for Sparse Estimation—0
Matrix Completion and Low-Rank SVD via Fast Alternating Least SquaresCode1
Individualized Rank Aggregation using Nuclear Norm Regularization—0
Generalized Low Rank ModelsCode1
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
Adaptive Multinomial Matrix Completion—0
A General Framework for Fast Stagewise Algorithms—0
Matrix Completion under Interval Uncertainty—0
Matrix Coherence and the Nystrom Method—0
Matrix Completion on GraphsCode0
Fast matrix completion without the condition number—0
On the Power of Adaptivity in Matrix Completion and Approximation—0
Relevance Singular Vector Machine for low-rank matrix sensing—0
Online Optimization for Large-Scale Max-Norm Regularization—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—0
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Bayesian matrix completion: prior specification—0
Spectral Unsupervised Parsing with Additive Tree Metrics—0
Distant Supervision for Relation Extraction with Matrix CompletionCode0
Depth Enhancement via Low-rank Matrix Completion—0
On Tensor Completion via Nuclear Norm Minimization—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix CompletionCode0
CUR Algorithm with Incomplete Matrix Observation—0
New Perspectives on k-Support and Cluster Norms—0
Computational Limits for Matrix Completion—0
Universal Matrix Completion—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
Low-Rank Modeling and Its Applications in Image Analysis—0
Online Matrix Completion Through Nuclear Norm Regularisation—0
Understanding Alternating Minimization for Matrix Completion—0
Matrix Completion From any Given Set of Observations—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning—0
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms—0
A Novel Two-Step Method for Cross Language Representation Learning—0
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions—0
The Noisy Power Method: A Meta Algorithm with Applications—0
Identifying Influential Entries in a Matrix—0
Unsupervised Spectral Learning of WCFG as Low-rank Matrix Completion—0
Incoherence-Optimal Matrix Completion—0
Online Algorithms for Factorization-Based Structure from Motion—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
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