Abstract
We have observed an interesting, yet unexplained, phenomenon: Semidefinite programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend to be tight in recovery problems with noisy data, even when MLE cannot exactly recover the ground truth. Several results establish tightness of SDP based relaxations in the regime where exact recovery from MLE is possible. However, to the best of our knowledge, their tightness is not understood beyond this regime. As an illustrative example, we focus on the generalized Procrustes problem.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1265-1267 |
| Number of pages | 3 |
| Journal | Journal of Machine Learning Research |
| Volume | 35 |
| State | Published - 2014 |
| Event | 27th Conference on Learning Theory, COLT 2014 - Barcelona, Spain Duration: Jun 13 2014 → Jun 15 2014 |
Keywords
- Convex relaxations
- Maximum likelihood estimator
- Procrustes problem
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence