The topic of this tutorial is Least Squares Sparse Principal Components Analysis (LS SPCA) which is a simple method for computing approximated Principal Components which are combinations of only a few of the observed variables. Analogously to Principal Components, these components are uncorrelated and sequentially best approximate the dataset. The derivation of LS SPCA is intuitive for anyone familiar with linear regression. Since LS SPCA is based on a different optimality from other SPCA methods and does not suffer from their serious drawbacks. I will demonstrate on two datasets how useful and parsimonious sparse PCs can be computed. An R package for computing LS SPCA is available for download.
翻译:本教程的主题是最小平方偏差主构件分析(LSSPCA),这是一个计算近似主构件的简单方法,只有少数观察变量的组合。与主构件类似,这些构件与主构件没有关联,顺序上最接近数据集。LS SPCA的衍生对熟悉线性回归的任何人来说都是不直观的。由于LS SPCA与其他SPCA方法不同,具有不同的最佳性,没有严重缺陷。我将用两套数据集来说明如何计算有用和稀有的PC。计算 LS SPCA的R包可以下载。