{"title":"最优正交群同步和旋转群同步","authors":"Chao Gao;Anderson Y Zhang","doi":"10.1093/imaiai/iaac022","DOIUrl":null,"url":null,"abstract":"We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization. The model is \n<tex>$Y_{ij} = Z_i^* Z_j^{*T} + \\sigma W_{ij}\\in{\\mathbb{R}}^{d\\times d}$</tex>\n where \n<tex>$W_{ij}$</tex>\n is a Gaussian random matrix and \n<tex>$Z_i^*$</tex>\n is either an orthogonal matrix or a rotation matrix, and each \n<tex>$Y_{ij}$</tex>\n is observed independently with probability \n<tex>$p$</tex>\n. We analyze an iterative polar decomposition algorithm for the estimation of \n<tex>$Z^*$</tex>\n and show it has an error of \n<tex>$(1+o(1))\\frac{\\sigma ^2 d(d-1)}{2np}$</tex>\n when initialized by spectral methods. A matching minimax lower bound is further established that leads to the optimality of the proposed algorithm as it achieves the exact minimax risk.","PeriodicalId":45437,"journal":{"name":"Information and Inference-A Journal of the Ima","volume":"12 2","pages":"591-632"},"PeriodicalIF":1.4000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Optimal orthogonal group synchronization and rotation group synchronization\",\"authors\":\"Chao Gao;Anderson Y Zhang\",\"doi\":\"10.1093/imaiai/iaac022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization. The model is \\n<tex>$Y_{ij} = Z_i^* Z_j^{*T} + \\\\sigma W_{ij}\\\\in{\\\\mathbb{R}}^{d\\\\times d}$</tex>\\n where \\n<tex>$W_{ij}$</tex>\\n is a Gaussian random matrix and \\n<tex>$Z_i^*$</tex>\\n is either an orthogonal matrix or a rotation matrix, and each \\n<tex>$Y_{ij}$</tex>\\n is observed independently with probability \\n<tex>$p$</tex>\\n. We analyze an iterative polar decomposition algorithm for the estimation of \\n<tex>$Z^*$</tex>\\n and show it has an error of \\n<tex>$(1+o(1))\\\\frac{\\\\sigma ^2 d(d-1)}{2np}$</tex>\\n when initialized by spectral methods. A matching minimax lower bound is further established that leads to the optimality of the proposed algorithm as it achieves the exact minimax risk.\",\"PeriodicalId\":45437,\"journal\":{\"name\":\"Information and Inference-A Journal of the Ima\",\"volume\":\"12 2\",\"pages\":\"591-632\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information and Inference-A Journal of the Ima\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10058607/\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Inference-A Journal of the Ima","FirstCategoryId":"100","ListUrlMain":"https://ieeexplore.ieee.org/document/10058607/","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 7
摘要
研究了正交群同步和旋转群同步的统计估计问题。该模型为$Y_{ij}=Z_i^*Z_j^{*T}+\mathbb{R}}^{d \ times d}$中的σW_{ij}$,其中$W_{ij}$是高斯随机矩阵,$Z_i^**$是正交矩阵或旋转矩阵,并且每个$Y_。我们分析了一种用于$Z^*$估计的迭代极分解算法,并表明当用谱方法初始化时,它的误差为$(1+o(1))\frac{\sigma^2 d(d-1)}{2np}$。进一步建立了匹配的极小极大下界,该下界导致所提出的算法的最优性,因为它实现了精确的极小极大风险。
Optimal orthogonal group synchronization and rotation group synchronization
We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization. The model is
$Y_{ij} = Z_i^* Z_j^{*T} + \sigma W_{ij}\in{\mathbb{R}}^{d\times d}$
where
$W_{ij}$
is a Gaussian random matrix and
$Z_i^*$
is either an orthogonal matrix or a rotation matrix, and each
$Y_{ij}$
is observed independently with probability
$p$
. We analyze an iterative polar decomposition algorithm for the estimation of
$Z^*$
and show it has an error of
$(1+o(1))\frac{\sigma ^2 d(d-1)}{2np}$
when initialized by spectral methods. A matching minimax lower bound is further established that leads to the optimality of the proposed algorithm as it achieves the exact minimax risk.