Gaussian graphical models are widely used to represent correlations among entities but remain vulnerable to data corruption. In this work, we introduce a modified trimmed-inner-product algorithm to robustly estimate the covariance in an online scenario even in the presence of arbitrary and adversarial data attacks. At each time step, data points, drawn nominally independently and identically from a multivariate Gaussian distribution, arrive. However, a certain fraction of these points may have been arbitrarily corrupted. We propose an online algorithm to estimate the sparse inverse covariance (i.e., precision) matrix despite this corruption. We provide the error-bound and convergence properties of the estimates to the true precision matrix under our algorithms.

翻译：高斯图模型广泛用于表示实体间的相关性，但易受数据损坏影响。本文提出一种改进的修剪内积算法，可在任意对抗性数据攻击下稳健估计在线场景中的协方差。在每个时间步，名义上独立同分布于多元高斯分布的数据点陆续到达，但其中一定比例的数据点可能被任意篡改。我们提出一种在线算法，即使在数据损坏的情况下也能估计稀疏逆协方差（即精度）矩阵。文中给出了该算法下估计量收敛至真实精度矩阵的误差界与收敛性质。