  Machine learning (ML) can process large sets of data generated from complex
systems, which is ideal for classification tasks as often appeared in critical
phenomena. Meanwhile ML techniques have been found effective in detecting
critical points, or in a broader sense phase separation, and extracting
critical exponents. But there are still many unsolved issues with the ML, one
of which is the meaning of hidden variables of unsupervised learning. Some say
that the hidden variables and the principal component may contain basic
information regarding the order parameter of the system of interest, which
sounds plausible but lacks evidence. This study aims at searching for evidence
supporting the conjecture that the autoencoder's (AE) single latent variable
and PCA's first principal component can only serve as signals related to
particle density, which happens to be the order parameter of the
non-equilibrium DP model. Indeed, in some phase transition (PT) models the
order parameter is the particle density, whereas in some PT models it is not.
Having conducted a certain degree of random shuffling on the DP configurations,
which are then fed to the neural networks as input, we find that AE's single
latent variable and PCA's first principal component can indeed represent
particle density. It is found that shuffling does affect the size of maximum
cluster in the system, which suggests that the second principal component of
the PCA is related to the maximal cluster. This has been supported by changes
in the correlation length of the transition system with variations in the
shuffle ratio.
