A recent line of research termed" unlabeled sensing" and" shuffled linear regression" has
been exploring under great generality the recovery of signals from subsampled and …

Linear regression without correspondences is the problem of performing a linear regression
fit to a dataset for which the correspondences between the independent samples and the …

Linear regression without correspondences concerns the recovery of a signal in the linear
regression setting, where the correspondences between the observations and the linear …

Abstract\emph {Unlabeled sensing} is a recent problem encompassing many data science
and engineering applications and typically formulated as solving linear equations whose …

In this paper, the unlabeled target localization problem in a wireless sensor network is
addressed. Sensors of the network are deployed in a two-dimensional surveyed area and …

摘要 Given two endomorphisms τ1, τ2 of ℂm, we provide eigenspace conditions under
which τ1 (v1)= τ2 (v2) for v1, v2∈ can only be true if v1= v2, where is a general n …

Unlabeled sensing is a linear inverse problem with permuted measurements. We propose
an alternating minimization (AltMin) algorithm with a suitable initialization for two widely …

The unlabeled sensing problem is to solve a noisy linear system of equations under
unknown permutation of the measurements. We study a particular case of the problem …

Mismatches between samples and their respective channel or target commonly arise in
several real-world applications. For instance, whole-brain calcium imaging of freely moving …

In this paper, we present a learning-based approach for solving shuffled linear systems in
header-free communication, thereby supporting low-latency communication. The resulting …