Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution
Table 1: Average error rates and standard deviations for supervised authorship attribution over a 5-fold cross-validation.
['size of training set', 'SMM-VAE', 'GMM-VAE', 'SVM (linear)', 'SVM (RBF kernel)']
It can be seen that our Student-t model is able to (slightly) outperform all baseline methods. For all methods, the performance gradually improves as the number of training pairs is increased. In addition, Fig. w.r.t. the dimension of the latent variable, where we can make the following observation: the best choice for D increases when more reviews are added to the training set. For 20% of the training data, the optimal dimension is D=50, for 40% we have D=100 and for more than 60%, D=200 yields the lowest error rate.