This is the reverse of my previous question Is PCA invariant to orthogonal transformations?
Let $A$ and $B$ be two $n$ x $p$ data matrices where $n$ is the number of samples and $p$ is the number of features.
Both $A$ and $B$ are centred (zero mean for each feature).
The following are the eigenvalue decompositions of their covariance matrices:
$$ \frac{1}{n-1} A^T A = V_A L_A V^T_A $$
$$ \frac{1}{n-1} B^T B = V_B L_B V^T_B $$
Now suppose that $A$ and $B$ have the same principal components, that is:
$$ A V_A = B V_B $$
Does it follow that $B$ is a rotation of $A$ (or vice-versa), that is:
$$ B = AQ \ ? $$