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.

• Publication year

  2021

• Venue

  Engineering Proceedings

• Publication date

  2021-07-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3390/ENGPROC2021005030
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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