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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 261–270 of 796 papers

TitleStatusHype
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fast Exact Matrix Completion with Finite Samples—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
Results on the algebraic matroid of the determinantal variety—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Fast matrix completion without the condition number—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
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