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Statistics > Machine Learning

arXiv:2206.08994 (stat)
[Submitted on 17 Jun 2022]

Title:Robust Group Synchronization via Quadratic Programming

Authors:Yunpeng Shi, Cole Wyeth, Gilad Lerman
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Abstract:We propose a novel quadratic programming formulation for estimating the corruption levels in group synchronization, and use these estimates to solve this problem. Our objective function exploits the cycle consistency of the group and we thus refer to our method as detection and estimation of structural consistency (DESC). This general framework can be extended to other algebraic and geometric structures. Our formulation has the following advantages: it can tolerate corruption as high as the information-theoretic bound, it does not require a good initialization for the estimates of group elements, it has a simple interpretation, and under some mild conditions the global minimum of our objective function exactly recovers the corruption levels. We demonstrate the competitive accuracy of our approach on both synthetic and real data experiments of rotation averaging.
Comments: Accepted to ICML 2022
Subjects: Machine Learning (stat.ML); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Numerical Analysis (math.NA)
MSC classes: 90C26, 90C17, 68Q87, 65C20, 90-08, 60-08
ACM classes: G.1.6; I.4.0
Cite as: arXiv:2206.08994 [stat.ML]
  (or arXiv:2206.08994v1 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2206.08994

Submission history

From: Yunpeng Shi [view email]
[v1] Fri, 17 Jun 2022 20:08:03 UTC (1,567 KB)
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