I have data, when I normalize it and then performed PCA, I calculated the variance of PC components, I found that, the first component is 72% and seconed component is 8% (total 72+8=80%) and so on.
Now I used to the same data and performed some data cleaning function and then normalize it and then performed PCA, calculated variance of the PCA components I found that, first component gives 40% and second component gives 20% (40+20=60%).
What does this mean? Which one is better? I found that, after data cleaning operation, the classifier gives highest accuracy. I am unable to have a sense what is happening exactly and why? Would anyone explain?