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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 201–225 of 796 papers

TitleStatusHype
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs—0
Deterministic Symmetric Positive Semidefinite Matrix Completion—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Differentially Private Matrix Completion Revisited—0
Discovering Abstract Symbolic Relations by Learning Unitary Group Representations—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Distributed Representations for Building Profiles of Users and Items from Text Reviews—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Doubly Robust Inference in Causal Latent Factor Models—0
Doubly robust nearest neighbors in factor models—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Background Subtraction via Fast Robust Matrix Completion—0
Efficient Federated Low Rank Matrix Completion—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Efficiently escaping saddle points on manifolds—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
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