Variational Autoencoder with Embedded Student-$t$ Mixture Model for Authorship Attribution

Understand

Traditional computational authorship attribution describes a classification task in a closed-set scenario.

• Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts.
• In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task.
• More precisely, we are extending a variational autoencoder (VAE) with embedded Gaussian mixture model to a Student-$t$ mixture model.