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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 276–300 of 796 papers

TitleStatusHype
CUR Algorithm for Partially Observed Matrices—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Crowdsourcing with Sparsely Interacting Workers—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees—0
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models—0
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
Counterfactual inference for sequential experiments—0
Convolutional Geometric Matrix Completion—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion—0
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Constructing High Frequency Economic Indicators by Imputation—0
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
A Denoising View of Matrix Completion—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Conservative Stochastic Optimization with Expectation Constraints—0
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