Message passing least squares framework and its application to rotation synchronization

Yunpeng Shi; Gilad Lerman

Message passing least squares framework and its application to rotation synchronization

Yunpeng Shi
, Gilad Lerman

School of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

27 Scopus citations

Abstract

We propose an efficient algorithm for solving group synchronization under high levels of corruption and noise, while we focus on rotation synchronization. We first describe our recent theoretically guaranteed message passing algorithm that estimates the corruption levels of themeasured group ratios. We then propose a novel reweighted least squares method to estimate the group elements, where the weights are initialized and iteratively updated using the estimated corruption levels. We demonstrate the superior performance of our algorithm over state-of-the-art methods for rotation synchronization using both synthetic and real data.

Original language	English (US)
Title of host publication	37th International Conference on Machine Learning, ICML 2020
Editors	Hal Daume, Aarti Singh
Publisher	International Machine Learning Society (IMLS)
Pages	8755-8765
Number of pages	11
ISBN (Electronic)	9781713821120
State	Published - 2020
Event	37th International Conference on Machine Learning, ICML 2020 - Virtual, Online Duration: Jul 13 2020 → Jul 18 2020

Publication series

Name	37th International Conference on Machine Learning, ICML 2020
Volume	PartF168147-12

Conference

Conference	37th International Conference on Machine Learning, ICML 2020
City	Virtual, Online
Period	7/13/20 → 7/18/20

Bibliographical note

Funding Information:
This work was supported by NSF award DMS-18-21266. We thank Tyler Maunu for his valuable comments on an earlier version of this manuscript.

Publisher Copyright:
© 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

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Message passing least squares framework and its application to rotation synchronization. / Shi, Yunpeng; Lerman, Gilad.
37th International Conference on Machine Learning, ICML 2020. ed. / Hal Daume; Aarti Singh. International Machine Learning Society (IMLS), 2020. p. 8755-8765 (37th International Conference on Machine Learning, ICML 2020; Vol. PartF168147-12).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shi, Y & Lerman, G 2020, Message passing least squares framework and its application to rotation synchronization. in H Daume & A Singh (eds), 37th International Conference on Machine Learning, ICML 2020. 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-12, International Machine Learning Society (IMLS), pp. 8755-8765, 37th International Conference on Machine Learning, ICML 2020, Virtual, Online, 7/13/20.

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AB - We propose an efficient algorithm for solving group synchronization under high levels of corruption and noise, while we focus on rotation synchronization. We first describe our recent theoretically guaranteed message passing algorithm that estimates the corruption levels of themeasured group ratios. We then propose a novel reweighted least squares method to estimate the group elements, where the weights are initialized and iteratively updated using the estimated corruption levels. We demonstrate the superior performance of our algorithm over state-of-the-art methods for rotation synchronization using both synthetic and real data.

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M3 - Conference contribution

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ER -

Message passing least squares framework and its application to rotation synchronization

Abstract

Publication series

Conference

Bibliographical note

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