  In this paper, we implement Principal Component Analysis (PCA) to study the
single particle distributions generated from thousands of {\tt VISH2+1}
hydrodynamic simulations with an aim to explore if a machine could directly
discover flow from the huge amount of data without explicit instructions from
human-beings. We found that the obtained PCA eigenvectors are similar to but
not identical with the traditional Fourier bases. Correspondingly, the PCA
defined flow harmonics $v_n^\prime$ are also similar to the traditional $v_n$
for $n=2$ and 3, but largely deviated from the Fourier ones for $n\geq 4$. A
further study on the symmetric cumulants and the Pearson coefficients indicates
that mode-coupling effects are reduced for these flow harmonics defined by PCA.
