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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 151–160 of 796 papers

TitleStatusHype
Concentration of tempered posteriors and of their variational approximations—0
Concentration properties of fractional posterior in 1-bit matrix completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion—0
Conservative Stochastic Optimization with Expectation Constraints—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Adaptively-weighted Nearest Neighbors for Matrix Completion—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
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