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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 81–90 of 796 papers

TitleStatusHype
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
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
The heterogeneous impact of the EU-Canada agreement with causal machine learning—0
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