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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 561–570 of 796 papers

TitleStatusHype
A Sparse Interactive Model for Matrix Completion with Side Information—0
Mistake Bounds for Binary Matrix Completion—0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Distributed Representations for Building Profiles of Users and Items from Text Reviews—0
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling—0
A Unified Convex Surrogate for the Schatten-p Norm—0
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information—0
Preference Completion from Partial Rankings—0
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