Logic-Oriented Autoencoders and Granular Logic Autoencoders: Developing Interpretable Data Representation

The plethora of ways of data representation and their applications to system modeling is inherently associated with dimensionality reduction. In a nutshell, the result of dimensionality reduction should support efficient ways of constructing ensuing models (classifiers and predictors) as well as an interpretation of the data themselves. Furthermore, there should be a suitable measure quantifying t...