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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 776–796 of 796 papers

TitleStatusHype
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Cluster Developing 1-Bit Matrix Completion—0
Clustering of Nonnegative Data and an Application to Matrix Completion—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Unorganized Malicious Attacks Detection—0
Collaborative Self-Attention for Recommender Systems—0
SP2: A Second Order Stochastic Polyak Method—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
Completing Any Low-rank Matrix, Provably—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
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