Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Refereed conference paper presented and published in conference proceedings


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AbstractEstimating covariance matrices is a fundamental technique in various domains, most notably in machine learning and signal processing. To tackle the challenges of extensive communication costs, large storage capacity requirements, and high processing time complexity when handling massive high-dimensional and distributed data, we propose an efficient and accurate covariance matrix estimation method via data compression. In contrast to previous data-oblivious compression schemes, we leverage a data-aware weighted sampling method to construct lowdimensional
data for such estimation. We rigorously prove that our proposed estimator is unbiased and requires smaller data to achieve the same accuracy with specially designed sampling distributions. Besides, we depict that the computational procedures in our algorithm are efficient. All achievements imply an improved tradeoff between the estimation accuracy and computational costs. Finally, the extensive experiments on synthetic and real-world datasets validate the superior property of our method and illustrate that it significantly outperforms the state-of-the-art algorithms.
All Author(s) ListXixian CHEN, Michael R. LYU, Irwin KING
Name of ConferenceThe 34th International Conference on Machine Learning (ICML)
Start Date of Conference06/08/2017
End Date of Conference11/08/2017
Place of ConferenceSydney
Country/Region of ConferenceAustralia
Proceedings TitleProceedings of the 34th International Conference on Machine Learning
Year2017
Month8
Volume Number70
Pages767 - 776
LanguagesEnglish-United States

Last updated on 2018-20-01 at 18:59