  We apply principal component analysis, a method frequently used in image
processing and unsupervised machine learning, to characterize particle
displacements observed in the steady shear flow of amorphous solids. PCA
produces a low-dimensional representation of the data and clearly reveals the
dominant features of elastic (i.e. reversible) and plastic deformation. We show
that the principal directions of PCA in the plastic regime correspond to the
soft (i.e. zero energy) modes of the elastic propagator that governs the
redistribution of shear stress due to localized plastic events. Projections
onto these soft modes also correspond to components of the displacement
structure factor at the first nonzero wavevectors, in close analogy to PCA
results for thermal phase transitions in conserved Ising spin systems. The
study showcases the ability of PCA to identify physical observables related to
the broken symmetry in a dynamical nonequilibrium transition.
