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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 271–280 of 796 papers

TitleStatusHype
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Data-based system representations from irregularly measured data—0
A two-dimensional decomposition approach for matrix completion through gossip—0
CUR Algorithm with Incomplete Matrix Observation—0
CUR Algorithm for Partially Observed Matrices—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Crowdsourcing with Sparsely Interacting Workers—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
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