Original paper
Linear Regression Without Correspondences via Concave Minimization
Abstract
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 reformulate it as a concave minimization problem, which we solve via branch-and-bound. This is...
Paper Details
Title
Linear Regression Without Correspondences via Concave Minimization
Published Date
Jan 1, 2020
Volume
27
Pages
1580 - 1584
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