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arXiv 2007.136382020-07cs.CVcs.ITmath.ITstat.ML

Message Passing Least Squares Framework and its Application to Rotation Synchronization

University of Minnesota（明尼苏达大学）；School of Mathematics, University of Minnesota（明尼苏达大学数学学院）

Yunpeng Shi, Gilad Lerman

备注：: To Appear in ICML 2020 Proceedings
发表信息：: International Conference on Machine Learning, 8796-8806 (2020)

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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 the measured 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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