  We demonstrate the identification and classification of topological phase
transitions from experimental data using Diffusion Maps: a nonlocal
unsupervised machine learning method. We analyze experimental data from an
optical system undergoing a topological phase transition and demonstrate the
ability of this approach to identify topological phase transitions even when
the data originates from a small part of the system, and does not even include
edge states.
