  Detecting structure in data is the first step to arrive at meaningful
representations for systems. This is particularly challenging for dislocation
networks evolving as a consequence of plastic deformation of crystalline
systems. Our study employs Isomap, a manifold learning technique, to unveil the
intrinsic structure of high-dimensional density field data of dislocation
structures from different compression axis. The resulting maps provide a
systematic framework for quantitatively comparing dislocation structures,
offering unique fingerprints based on density fields. Our novel, unbiased
approach contributes to the quantitative classification of dislocation
structures which can be systematically extended.
