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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 31–40 of 796 papers

TitleStatusHype
Matrix Completion via Residual Spectral Matching—0
Representational Transfer Learning for Matrix Completion—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
On adaptivity and minimax optimality of two-sided nearest neighborsCode0
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—0
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein SpaceCode0
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