Covariance estimation for multidimensional data using the EM algorithm

Proceedings of 27th Asilomar Conference on Signals, Systems and Computers Pub Date : 1993-11-01 DOI:10.1109/ACSSC.1993.342500

T. A. Barton, D. Fuhrmann

引用次数: 8

Abstract

Under a complex-Gaussian data model, a maximum likelihood method based on the iterative expectation-maximization algorithm is given to estimate structured covariance matrices for multidimensional data organized into column-vector form. The covariance structures of interest involve a hierarchy of subblocks within the covariance matrix, and include block-circulant and block Toeplitz matrices and their generalizations. These covariance matrices are elements of certain covariance constraint sets such that each element may be described as a matrix multiplication of a known matrix of Kronecker products and a nonnegative-definite, block-diagonal matrix. Several convergence properties of the estimation procedure are discussed, and an example of algorithm behavior is provided.<>

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于EM算法的多维数据协方差估计

在复高斯数据模型下，给出了一种基于迭代期望最大化算法的极大似然方法，用于估计列向量形式多维数据的结构化协方差矩阵。感兴趣的协方差结构涉及协方差矩阵中的子块层次结构，包括块循环矩阵和块Toeplitz矩阵及其推广。这些协方差矩阵是某些协方差约束集合的元素，使得每个元素可以被描述为已知的克罗内克积矩阵和非负定的块对角矩阵的矩阵乘法。讨论了估计过程的几个收敛性，并给出了算法行为的一个例子

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Proceedings of 27th Asilomar Conference on Signals, Systems and Computers

自引率

0.00%

发文量

期刊最新文献

Estimating error diffusion kernel from error diffused images Optical-to-SAR image registration using the active contour model Efficient organization of large ship radar databases using wavelets and structured vector quantization Signal processing using the generalized Taylor series expansion Evaluation of a model based data fusion algorithm with multi-mode OTHR data