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Feb, 2017
基于阈值的高效异常值鲁棒主成分分析
Thresholding based Efficient Outlier Robust PCA
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Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
TL;DR
本文提出了基于阈值的迭代算法,用于处理含有离群点的数据集中的偏差鲁棒主成分分析问题,该方法的迭代复杂度最多线性,可以处理至多α分数的离群点,并且对于一般噪声设置具有近乎最佳的计算复杂度。对于特殊情况,即噪声是加性高斯噪声,本文改进了该方法,并成功地减小了恢复误差。
Abstract
We consider the problem of
outlier robust pca
(OR-PCA) where the goal is to recover
principal directions
despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-\alpha)$ fractio
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