Variational Deep Embedding with Regularized Student-t Mixture Model

Abstract

This paper proposes a new motion classifier using variational deep embedding with regularized student-t mixture model as prior, named VaDE-RT, to improve robustness to outliers while maintaining continuity in latent space. Normal VaDE uses Gaussian mixture model, which is sensitive to outliers, and furthermore, all the components of mixture model can freely move in the latent space, which would lose the continuity in the latent space. In contrast, VaDE-RT aims to exploit a heavy-tailed feature of student-t distribution for robustness, and regularize the mixture model to standard normal distribution, which is employed in a standard variational autoencoder as prior. To do so, three reasonable approximations for (i) reparameterization trick, (ii) Kullback-Leibler (KL) divergence between student-t distributions, and (iii) KL divergence of the mixture model, are introduced to make backpropagation in VaDE-RT possible. As a result, VaDE-RT outperforms the original VaDE and a simple deep-learning-based classifier in terms of classification accuracy. In addition, VaDE-RT yields both continuity and natural topology of clusters in the latent space, which make robot control adaptive smoothly.