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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 126–150 of 796 papers

TitleStatusHype
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Adaptive Noisy Matrix Completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Approximating Concavely Parameterized Optimization Problems—0
A Harmonic Extension Approach for Collaborative Ranking—0
Adaptive Multinomial Matrix Completion—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Completing Any Low-rank Matrix, Provably—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Approximate matrix completion based on cavity method—0
Computational Graph Completion—0
Computational Limits for Matrix Completion—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
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