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Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder
At the beginning of 2020 the world has seen the initial outbreak of COVID-19, a disease caused by SARS-CoV2 virus in China.
Mixing autoencoder with classifier: conceptual data visualization
When it is trained as an autoencoder, the inherent topological structure of the data can be visualized, while when it is trained as a classifier, the topological structure is further constrained by the concept, for example the labels the data, hence the visualization is not only structural but also conceptual.
Dimension Reduction with Non-degrading Generalization
It is possible to classify the data in the high dimensional space, while visualizing them in the low dimensional space, but in this case, the visualization is often meaningless because it fails to illustrate the underlying characteristics that are crucial for the classification process.
Classifier with Hierarchical Topographical Maps as Internal Representation
In this study we want to connect our previously proposed context-relevant topographical maps with the deep learning community.
