Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics

Picking an appropriate parameter setting (meta-parameters) for visualization and embedding techniques is a tedious task. However, especially when studying the latent representation generated by an autoencoder for unsupervised data analysis, it is also an indispensable one. Here we present a procedure using a cross-correlative take on the meta-parameters. This ansatz allows us to deduce meaningful meta-parameter limits using OPTICS, DBSCAN, UMAP, t-SNE, and k-MEANS. We can perform first steps of a meaningful visual analysis in the unsupervised case using a vanilla autoencoder on the MNIST and DeepVALVE data sets.