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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 726–750 of 796 papers

TitleStatusHype
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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