I was wondering whether it makes sense to do PCA after robust PCA. Suppose I have a matrix $X$ and if I do robust PCA I would get:
And if I do PCA over $A$ would this make sense as a preprocessing step? The matrix $X$ is highly sparse (less than 10% is populated, with a size of 15K by 20K).
My objective is to reduce the dimensionality of $X$, but it contains some extreme outliers.