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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 41–50 of 796 papers

TitleStatusHype
Learning Counterfactual Distributions via Kernel Nearest NeighborsCode0
Can We Predict Performance of Large Models across Vision-Language Tasks?Code0
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures—0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Tailed Low-Rank Matrix Factorization for Similarity Matrix Completion—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows—0
Negative Binomial Matrix Completion—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Online Matrix Completion: A Collaborative Approach with Hott Items—0
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