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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
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
A Riemannian gossip approach to decentralized matrix completion—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
A two-dimensional decomposition approach for matrix completion through gossip—0
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation—0
A Unified Convex Surrogate for the Schatten-p Norm—0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
A Unified Framework for Structured Low-rank Matrix Learning—0
Autoencoder-based Graph Construction for Semi-supervised Learning—0
Multi-target prediction for dummies using two-branch neural networks—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Background Subtraction via Fast Robust Matrix Completion—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Smooth PARAFAC Decomposition for Tensor Completion—0
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