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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 151–160 of 796 papers

TitleStatusHype
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
A General Framework for Fast Stagewise Algorithms—0
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Active Feature Acquisition with Supervised Matrix Completion—0
Categorical Matrix Completion—0
Calibrated Elastic Regularization in Matrix Completion—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
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