Highdimensional Motion Segmentation by Variational Autoencoder and Gaussian Processes Humans perceive continuous highdimensional information by dividing it into significant segments such as words and units of motion. We believe that such unsupervised segmentation is also important for robots to learn topics such as language and motion. To this end, we previously proposed a hierarchical Dirichlet processxe2x80x93Gaussian processxe2x80x93hidden semiMarkov model (HDPGPHSMM). However, an important drawback to this model is that it cannot divide highdimensional timeseries data. Further, lowdimensional features must be extracted in advance. Segmentation largely depends on the design of features, and it is difficult to design effective features, especially in the case of highdimensional data. To overcome this problem, this paper proposes a hierarchical Dirichlet processxe2x80x93variational autoencoderxe2x80x93Gaussian processxe2x80x93hidden semiMarkov model (HVGH). The parameters of the proposed HVGH are estimated through a mutual learning loop of the variational autoencoder and our previously proposed HDPGPHSMM. Hence, HVGH can extract features from highdimensional timeseries data, while simultaneously dividing it into segments in an unsupervised manner. In an experiment, we used various motioncapture data to show that our proposed model estimates the correct number of classes and more accurate segments than baseline methods. Moreover, we show that the proposed method can learn latent space suitable for segmentation.

VSBDVM: An EndtoEnd Bayesian Nonparametric Generalization of Deep Variational Mixture Model Mixture of factor analyzers is a fundamental model in unsupervised learning, which is particularly useful for high dimensional data. Recent efforts on deep autoencoding mixture models made a fruitful progress in clustering. However, in most cases, their performance depends highly on the results of pretraining. Moreover, they tend to ignore the prior information when making clustering assignment, leading to a less strict inference and consequently limiting the performance. In this paper, we propose an endtoend Bayesian nonparametric generalization of deep mixture model with a Variational AutoEncoder (VAE) framework. Specifically, we develop a novel model called VSBDVM exploiting the Variational StickBreaking Process to design a Deep Variational Mixture Model. Distinct from the existing deep autoencoding mixture models, this novel unsupervised deep generative model can learn lowdimensional representations and clustering simultaneously without pretraining. Importantly, a strict inference is proposed using weights of stickbreaking process in a variational way. Furthermore, able to capture the richer statistical structure of the data, VSBDVM can also generate highly realistic samples for any specified cluster. A series of experiments are carried out, both qualitatively and quantitatively, on benchmark clustering and generation tasks. Comparative results show that the proposed model is able to generate diverse and highquality samples of data, and also achieves encouraging clustering results outperforming the stateoftheart algorithms on four realworld datasets.