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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 91–100 of 796 papers

Title	Date	Tasks	Status	Score
Guaranteed Rank Minimization via Singular Value Projection	Sep 30, 2009	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available	5
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression	May 24, 2016	Matrix Completionregression	CodeCode Available	5
Collaborative Filtering with Graph Information: Consistency and Scalable Methods	Dec 1, 2015	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available	5
Implicit Regularization in Deep Learning May Not Be Explainable by Norms	May 13, 2020	Deep LearningMatrix Completion	CodeCode Available	5
A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion	Oct 27, 2009	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available	5
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties	Jun 12, 2024	Matrix CompletionRepresentation Learning	CodeCode Available	5
Collective Matrix Completion	Jul 24, 2018	Matrix Completion	CodeCode Available	5
A Perturbation Bound on the Subspace Estimator from Canonical Projections	Jun 28, 2022	ClusteringMatrix Completion	CodeCode Available	5
Adaptive Matrix Completion for the Users and the Items in Tail	Apr 22, 2019	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available	5
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters	May 22, 2017	Community DetectionDeep Learning	CodeCode Available	5

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