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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 731–740 of 796 papers

TitleStatusHype
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
A two-dimensional decomposition approach for matrix completion through gossip—0
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation—0
A Unified Convex Surrogate for the Schatten-p Norm—0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
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