TR2021-047
Multiview Sensing with Unknown Permutations: An Optimal Transport Approach
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- "Multiview Sensing with Unknown Permutations: An Optimal Transport Approach", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP39728.2021.9415075, June 2021, pp. 1440-1444.BibTeX TR2021-047 PDF Video
- @inproceedings{Ma2021jun,
- author = {Ma, Yanting and Boufounos, Petros T. and Mansour, Hassan and Aeron, Shuchin},
- title = {Multiview Sensing with Unknown Permutations: An Optimal Transport Approach},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2021,
- pages = {1440--1444},
- month = jun,
- doi = {10.1109/ICASSP39728.2021.9415075},
- issn = {1520-6149},
- isbn = {978-1-7281-7606-2},
- url = {https://www.merl.com/publications/TR2021-047}
- }
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- "Multiview Sensing with Unknown Permutations: An Optimal Transport Approach", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP39728.2021.9415075, June 2021, pp. 1440-1444.
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Abstract:
In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to unknown permutations. In this paper we take a fresh look at this problem through the lens of optimal transport (OT). In particular, we recognize that in most practical applications the unknown permutations are not arbitrary but some are more likely to occur than others. We exploit this by introducing a regularization function that promotes the more likely permutations in the solution. We show that, even though the general problem is not convex, an appropriate relaxation of the resulting regularized problem allows us to exploit the well-developed machinery of OT and develop a tractable algorithm.