Matrix Completion
131 papers with code • 0 benchmarks • 4 datasets
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
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Netflix Prize
CAL500
Most implemented papers
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix Completion
Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training.
Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions
Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible.
Matrix Completion from a Few Entries
In the process of proving these statements, we obtain a generalization of a celebrated result by Friedman-Kahn-Szemeredi and Feige-Ofek on the spectrum of sparse random matrices.
Matrix Completion from Noisy Entries
Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries.
Guaranteed Rank Minimization via Singular Value Projection
Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics.
A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion
We consider the problem of reconstructing a low-rank matrix from a small subset of its entries.
Online Identification and Tracking of Subspaces from Highly Incomplete Information
GROUSE performs exceptionally well in practice both in tracking subspaces and as an online algorithm for matrix completion.
Robust PCA via Outlier Pursuit
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers.
Online Robust Subspace Tracking from Partial Information
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information.
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
