Sparse spectral estimation with missing and corrupted measurements

doi:10.1002/sta4.229

Sparse spectral estimation with missing and corrupted measurements

2019-06-26

0

OA

AI

DOI：10.1002/sta4.229

原文链接

查看原文

摘要

En 中文

Supervised learning methods with missing data have been extensively studied not just due to the techniques related to low‐rank matrix completion. Also, in unsupervised learning, one often relies on imputation methods. As a matter of fact, missing values induce a bias in various estimators such as the sample covariance matrix. In the present paper, a convex method for sparse subspace estimation is extended to the case of missing and corrupted measurements. This is done by correcting the bias instead of imputing the missing values. The estimator is then used as an initial value for a nonconvex procedure to improve the overall statistical performance. The methodological and theoretical frameworks are applied to a wide range of statistical problems. These include sparse principal component analysis with different types of randomly missing data. Finally, the statistical performance is demonstrated on synthetic data.

Sparse spectral estimation with missing and corrupted measurements

摘要

AI总结

期刊

机构

引用论文