$Y = WX$, if $\Sigma(X)$ is known and $\Sigma(Y)==I$ , then $W$ is a whitening transformation.
常见的W可以是： $L^T$ [$\Sigma(X)$的Cholesky decomposition factor], 或者直接就是 $\Sigma(X)^{-\frac{1}{2}}$
对于数据矩阵的白化：把每个feature当成一个随机变量，那么样本就是一个随机的向量；的一般是先用极大似然估计这组数据的协方差矩阵，再用Cholesky分解进行白化；
在自监督学习中，白化hidden representation是一种重要的方法：
The whitening operation has a “scattering” effect on the batch samples, avoiding degenerate solutions where all the sample representations collapse to a single point.
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