【讨论】关于主成分分析PCA的理解

Scalar是“标量”的意思，数学中相对于“向量”Vector

我尝试翻译一下：

PCA is a method for reducing the 100 variables (wavelength data) in each spectrum down to just a few important variables. These variables are often referred to as latent variables, principal components, factors, eigenvectors, etc, and are vectors. This manual will refer to them as PC’s. The dot product of these vectors with the spectral data yields scalars called “PC scores”. Unknowns can be identified by comparing the PC scores of unknown materials to those of the model.

PCA是将每一光谱中100个（头一次听说是100个？呵呵）变量（波长数据）减少到仅几个重要变量的一种方法。这些变量通常称为潜变量、主成分、因子、特征向量等（这4中说法在数学上是有区别的，而且在光谱应用中也不是对等的，此说法不够准确），都是向量。在本手册中将称为PCs。光谱数据的这些向量点积（向量的乘积有点乘和叉乘）就形成标量，称为PC得分。通过比较未知物质和模型物质的PC分值，就可以确定未知物质了。