异常值鲁棒主成分分析的近线性时间和流式算法（英文版）

英文标题：Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA中文摘要：本研究主要研究经典问题 PCA 中的异常值问题，提出了近线性时间的近似最优解鲁棒 PCA 算法以及单遍流式鲁棒 PCA 算法，并进行了相关的理论分析。英文摘要：We study principal component analysis (PCA), where given a dataset in$\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ thatapproximately maximizes the variance of the distribution after being projectedalong $v$. Despite being a classical task, standard estimators fail drasticallyif the data contains even a small fracti