102 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.
These leaderboards are used to track progress in Matrix Completion
LibrariesUse these libraries to find Matrix Completion models and implementations
The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.
Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.
Under the extreme setting where not any side information is available other than the matrix to complete, can we still learn an inductive matrix completion model?
Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.