Fast monte-carlo algorithms for finding low-rank approximations
Journal of the ACM (JACM)
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Theory of semidefinite programming for Sensor Network Localization
Mathematical Programming: Series A and B
Uncovering shared structures in multiclass classification
Proceedings of the 24th international conference on Machine learning
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The power of convex relaxation: near-optimal matrix completion
IEEE Transactions on Information Theory
Matrix completion from a few entries
IEEE Transactions on Information Theory
Matrix Completion from Noisy Entries
The Journal of Machine Learning Research
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Robust principal component analysis?
Journal of the ACM (JACM)
Fixed point and Bregman iterative methods for matrix rank minimization
Mathematical Programming: Series A and B
A Simpler Approach to Matrix Completion
The Journal of Machine Learning Research
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Recovering Low-Rank Matrices From Few Coefficients in Any Basis
IEEE Transactions on Information Theory
Trust prediction via aggregating heterogeneous social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Social trust prediction using heterogeneous networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Hi-index | 48.22 |
Suppose that one observes an incomplete subset of entries selected from a low-rank matrix. When is it possible to complete the matrix and recover the entries that have not been seen? We demonstrate that in very general settings, one can perfectly recover all of the missing entries from most sufficiently large subsets by solving a convex programming problem that finds the matrix with the minimum nuclear norm agreeing with the observed entries. The techniques used in this analysis draw upon parallels in the field of compressed sensing, demonstrating that objects other than signals and images can be perfectly reconstructed from very limited information.