  Principal component analysis (PCA) defines a reduced space described by PC
axes for a given multidimensional-data sequence to capture the variations of
the data. In practice, we need multiple data sequences that accurately obey
individual probability distributions and for a fair comparison of the sequences
we need PC axes that are common for the multiple sequences but properly capture
these multiple distributions. For these requirements, we present individual
ergodic samplings for these sequences and provide special reweighting for
recovering the target distributions.
