# R-local unlabeled sensing: A novel graph matching approach for multiview unlabeled sensing under local permutations

arXiv: https://arxiv.org/abs/1911.06382 · HTML: https://mlsanity.com/paper/1911.06382/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Shuffled Multi-Channel Sparse Signal Recovery](https://mlsanity.com/paper/2212.07368/) (2022)
- [Alternating minimization algorithm with initialization analysis for r-local and k-sparse unlabeled sensing](https://mlsanity.com/paper/2211.07621/) (2022)
- [Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces](https://mlsanity.com/paper/2506.09773/) (2025)

## Abstract (arXiv)

Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix. Motivated by practical tasks such as mobile sensor networks, target tracking and the pose and correspondence estimation between point clouds, we study a special case of this problem restricting the class of permutations to be local and allowing for multiple views. In this setting, namely unlabeled multi-view sensing with local permutation, previous results and algorithms are not directly applicable. In this paper, we propose a computationally efficient algorithm that creatively exploits the machinery of graph alignment and Gromov-Wasserstein alignment and leverages the multiple views to estimate the local permutations. Simulation results on synthetic data sets indicate that the proposed algorithm is scalable and applicable to the challenging regimes of low to moderate SNR.