Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics
| Autor: | Amala Paulson, Dirk Lehmann, Maria Walch, Peter Schichtel |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
DBSCAN
business.industry Computer science Dimensionality reduction Big data 020207 software engineering Pattern recognition 02 engineering and technology Autoencoder Visualization Analytics 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business MNIST database |
| Zdroj: | The 7th International conference on Time Series and Forecasting. |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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