What can we say about a dataset if we apply PCA and observe that there is a high percentage of variance in the first principal component(s)?
Can we say that this dataset has linear structure? Can we say that most of the features in this dataset are highly correlated?
For example, I extracted PC1 and PC2 for two high-dimensional datasets $A$ and $B$. For data set $A$, 95% of variance is explained by PC1 and PC2. Whereas for data set $B$, we only observed 40% of variance explained by PC1 and PC2. What can we infer about datasets $A$ and $B$?