  Finding the optimal model complexity that minimizes the generalization error
(GE) is a key issue of machine learning. For the conventional supervised
learning, this task typically involves the bias-variance tradeoff: lowering the
bias by making the model more complex entails an increase in the variance.
Meanwhile, little has been studied about whether the same tradeoff exists for
unsupervised learning. In this study, we propose that unsupervised learning
generally exhibits a two-component tradeoff of the GE, namely the model error
and the data error -- using a more complex model reduces the model error at the
cost of the data error, with the data error playing a more significant role for
a smaller training dataset. This is corroborated by training the restricted
Boltzmann machine to generate the configurations of the two-dimensional Ising
model at a given temperature and the totally asymmetric simple exclusion
process with given entry and exit rates. Our results also indicate that the
optimal model tends to be more complex when the data to be learned are more
complex.
