Influence in Principal Components Analysis
本文借鉴线性回归中的影响函数，开发了用于主成分分析中检测异常观测值的方法，并利用实对称矩阵的扰动理论统一了推导，指出了与回归情况的差异。
In linear regression, the theoretical influence function and the various sample versions of it have an established place as diagnostic tools. These same functions are developed here to provide methods for the detection of influential observations in principal components analysis. The perturbation theory of real symmetric matrices unifies this development. Some interesting points of contrast with the regression case are noted and explained theoretically.