  In condensed matter physics, one of the goals of machine learning is the
classification of phases of matter. The consideration of a system's symmetries
can significantly assist the machine in this goal. We demonstrate the ability
of an unsupervised machine learning protocol, the Principal Component Analysis
method, to detect hidden quenched gauge symmetries introduced via the so-called
Mattis gauge transformation. Our work reveals that unsupervised machine
learning can identify hidden properties of a model and may therefore provide
new insights into the models themselves.

Disordered Systems and Neural Networks;Computational Physics