Robust Inverse Covariance Estimation under Noisy Measurements
Robust Inverse Covariance Estimation under Noisy Measurements
复制标题
噪声测量下的鲁棒逆协方差估计
DOI：
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复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
Shou
中科院分区：
文献类型：
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作者：
Jun;Shou
本文提出了一种鲁棒的方法来估计噪声测量下的逆协方差。该方法是基于独立通过稳健回归估计逆协方差矩阵中的每列，这使得并行化。与以往基于线性规划的方法不能保证半正定协方差矩阵不同，我们的方法调整学习矩阵以满足这一条件，这进一步方便了预测未来值的任务。在噪声环境下的时间序列预测和分类实验表明了该方法的有效性。
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.