论文标题
改进了相关矩阵的近似和可视化
Improved approximation and visualization of the correlation matrix
论文作者
论文摘要
审查了通过不同的多元统计方法的相关矩阵的图形表示，对不同过程的比较与使用示例数据集的使用进行了比较，并提出了改进的具有更好拟合的表示。主成分分析被广泛用于制作相关结构的图片，尽管如图所示，一种加权交替的最小二乘方法避免了相关矩阵的对角线的拟合在近似相关矩阵时优于主成分分析和主因子分析。加权交替的最小二乘是主要组成部分分析的非常强大的竞争者，特别是如果相关矩阵是研究的重点，因为它可以改善相关矩阵的表示，通常仅以牺牲较小比例的原始数据矩阵的解释差异为代价，如果后者映射到correalitation biplot bipliplation bifipleds biallation biiplation biiplation biiplesions biallation biflipression。在本文中，我们建议将加权交替的最小二乘与相关矩阵的添加性调整相结合，这被认为导致相关矩阵的近似值进一步改善。
The graphical representation of the correlation matrix by means of different multivariate statistical methods is reviewed, a comparison of the different procedures is presented with the use of an example data set, and an improved representation with better fit is proposed. Principal component analysis is widely used for making pictures of correlation structure, though as shown a weighted alternating least squares approach that avoids the fitting of the diagonal of the correlation matrix outperforms both principal component analysis and principal factor analysis in approximating a correlation matrix. Weighted alternating least squares is a very strong competitor for principal component analysis, in particular if the correlation matrix is the focus of the study, because it improves the representation of the correlation matrix, often at the expense of only a minor percentage of explained variance for the original data matrix, if the latter is mapped onto the correlation biplot by regression. In this article, we propose to combine weighted alternating least squares with an additive adjustment of the correlation matrix, and this is seen to lead to further improved approximation of the correlation matrix.
