Linear Regression Without Correspondences via Concave Minimization
Linear Regression Without Correspondences via Concave Minimization
Linear regression without correspondences concerns the recovery of a signal in the linear regression setting, where the correspondences between the observations and the linear functionals are unknown. The associated maximum likelihood function is NP-hard to compute when the signal has dimension larger than one. To optimize this objective function we …