@inproceedings{6ef165ad6f544e51b21f30774bf0e2e1,
title = "Sequential inverse approximation of a regularized sample covariance matrix",
abstract = "One of the goals in scaling sequential machine learning methods pertains to dealing with high-dimensional data spaces. A key related challenge is that many methods heavily depend on obtaining the inverse covariance matrix of the data. It is well known that covariance matrix estimation is problematic when the number of observations is relatively small compared to the number of variables. A common way to tackle this problem is through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix, with the aim of minimizing the mean-squared error. We derived sequential update rules to approximate the inverse shrinkage estimator of the covariance matrix. The approach paves the way for improved large-scale machine learning methods that involve sequential updates.",
keywords = "covariance estimation, minimum mean squared error, shrinkage estimator",
author = "Tomer Lancewicki",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1109/ICMLA.2017.00-40",
language = "English",
series = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "903--907",
editor = "Xuewen Chen and Bo Luo and Feng Luo and Vasile Palade and Wani, \{M. Arif\}",
booktitle = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
address = "United States",
}