  We consider unsupervised learning methods for characterizing the disordered
microscopic structure of supercooled liquids and glasses. Specifically, we
perform dimensionality reduction of smooth structural descriptors that describe
radial and bond-orientational correlations, and assess the ability of the
method to grasp the essential structural features of glassy binary mixtures. In
several cases, a few collective variables account for the bulk of the
structural fluctuations within the first coordination shell and also display a
clear connection with the fluctuations of particle mobility. Fine-grained
descriptors that characterize the radial dependence of bond-orientational order
better capture the structural fluctuations relevant for particle mobility, but
are also more difficult to parametrize and to interpret. We also find that
principal component analysis of bond-orientational order parameters provides
identical results to neural network autoencoders, while having the advantage of
being easily interpretable. Overall, our results indicate that glassy binary
mixtures have a broad spectrum of structural features. In the temperature range
we investigate, some mixtures display well-defined locally favored structures,
which are reflected in bimodal distributions of the structural variables
identified by dimensionality reduction.
