Open AccessProceedings Article
Robust Inverse Covariance Estimation under Noisy Measurements
Jun-Kun Wang,Shou-De Lin +1 more
- pp 928-936
TLDR
Different from previous linear programming based methods that cannot guarantee a positive semi-definite covariance matrix, this method adjusts the learned matrix to satisfy this condition, which further facilitates the tasks of forecasting future values.Abstract:
This paper proposes a robust method to estimate the inverse covariance under noisy measurements. The method is based on the estimation of each column in the inverse covariance matrix independently via robust regression, which enables parallelization. Different from previous linear programming based methods that cannot guarantee a positive semi-definite covariance matrix, our method adjusts the learned matrix to satisfy this condition, which further facilitates the tasks of forecasting future values. Experiments on time series prediction and classification under noisy condition demonstrate the effectiveness of the approach.read more
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References
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Model selection and estimation in regression with grouped variables
Ming Yuan,Yi Lin +1 more
TL;DR: In this paper, instead of selecting factors by stepwise backward elimination, the authors focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection.
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