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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 711–720 of 796 papers

TitleStatusHype
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
A Note on Alternating Minimization Algorithm for the Matrix Completion Problem—0
A note on the statistical view of matrix completion—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A Novel Two-Step Method for Cross Language Representation Learning—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Approximate matrix completion based on cavity method—0
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