Efficient Distributed Estimation of Inverse Covariance Matrices
In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency. In this paper, we are interested in estimating sparse inverse covariance matrices when samples are distributed into different machines. We address communication efficiency by proposing a method where, in a single round of communication, each machine transfers a small subset of the entries of the inverse covariance matrix. We show that, with this efficient distributed method, the error rates can be comparable with estimation in a non-distributed setting, and correct model selection is still possible. Practical performance is shown through simulations.
Code (0)
등록된 구현이 없습니다.
Tasks
Model SelectionSimilar Papers 제목 키워드 기반
Joint Inverse Covariances Estimation with Mutual Linear Structure
We consider the problem of joint estimation of structured inverse covariance matrices. We perform the estimation using groups of measurements with different covariances of the same unknown structure. Assuming the inverse…
Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses
When only few data samples are accessible, utilizing structural prior knowledge is essential for estimating covariance matrices and their inverses. One prominent example is knowing the covariance matrix to be Toeplitz st…
Inverse Covariance and Partial Correlation Matrix Estimation via Joint Partial Regression
We present a method for estimating sparse high-dimensional inverse covariance and partial correlation matrices, which exploits the connection between the inverse covariance matrix and linear regression. The method is a t…
Minimax Estimation of Bandable Precision Matrices
The inverse covariance matrix provides considerable insight for understanding statistical models in the multivariate setting. In particular, when the distribution over variables is assumed to be multivariate normal, the …
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices
In this paper, we address the problem of jointly estimating dependencies across samples and dependencies across multiple features, where each set of dependencies is modeled as an inverse covariance matrix. In particular,…