For example, Matos et al. used principal component analysis (PCA) to visualize the independent effect of crystallite size and orientation on an XRD pattern. PCA identifies linear transformations of the data with high variance, such as select peaks decreasing in intensity with increasing orientation. The effect of this transformation is to reduce the dataset to only the most important dimensions (e.g., the size and degree of orientation). CNN autoencoders have been similarly used as feature extractors. Banko et al. used autoencoders to understand the distribution of data among different space groups, which form distinct clusters in the information dense latent space.