Effective Data-aware Covariance Estimator from Compressed Data
Machine Learning
2020-10-13 v1 Machine Learning
Abstract
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted sampling based covariance matrix estimator, namely DACE, which can provide an unbiased covariance matrix estimation and attain more accurate estimation under the same compression ratio. Moreover, we extend our proposed DACE to tackle multiclass classification problems with theoretical justification and conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superior performance of our DACE.
Cite
@article{arxiv.2010.04966,
title = {Effective Data-aware Covariance Estimator from Compressed Data},
author = {Xixian Chen and Haiqin Yang and Shenglin Zhao and Michael R. Lyu and Irwin King},
journal= {arXiv preprint arXiv:2010.04966},
year = {2020}
}
Comments
12 pages, 5 figures