The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Matrix completion models are among the most common formulations of recommender systems.
#3 best model for Recommendation Systems on MovieLens 100K (using extra training data)
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings.
SOTA for Recommendation Systems on Douban
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.
#8 best model for Recommendation Systems on MovieLens 1M
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?