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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 651–660 of 796 papers

TitleStatusHype
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Scalable and Robust Community Detection with Randomized Sketching—0
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation—0
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
Recent Developments on Factor Models and its Applications in Econometric Learning—0
Recognizing retinal ganglion cells in the dark—0
Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization—0
Recommendation via matrix completion using Kolmogorov complexity—0
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