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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
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs—0
Matrix Completion of World Trade—0
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization—0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
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