  The characterization of diffusion processes is a keystone in our
understanding of a variety of physical phenomena. Many of these deviate from
Brownian motion, giving rise to anomalous diffusion. Various theoretical models
exists nowadays to describe such processes, but their application to
experimental setups is often challenging, due to the stochastic nature of the
phenomena and the difficulty to harness reliable data. The latter often
consists on short and noisy trajectories, which are hard to characterize with
usual statistical approaches. In recent years, we have witnessed an impressive
effort to bridge theory and experiments by means of supervised machine learning
techniques, with astonishing results. In this work, we explore the use of
unsupervised methods in anomalous diffusion data. We show that the main
diffusion characteristics can be learnt without the need of any labelling of
the data. We use such method to discriminate between anomalous diffusion models
and extract their physical parameters. Moreover, we explore the feasibility of
finding novel types of diffusion, in this case represented by compositions of
existing diffusion models. At last, we showcase the use of the method in
experimental data and demonstrate its advantages for cases where supervised
learning is not applicable.
