GPGPU Implementation of Variational Bayesian Gaussian Mixture Models Machine learning technology has been developed along with the performance improvement of CPUs and GPGPUs, and it has been used for analyses of big data. Clustering is one task of them. Gaussian Mixture Models are a representation method that realizes clustering, and widely used in many applications for probability density modeling and soft clustering. However, the parameters of a GMM need to be estimated from data. There are some methods for estimate parameters, Variational Bayesian Gaussian Mixture Models (VBGMM) are one of them, and it is hardly to overfitting than other methods. But, it is computationally demanding. In this paper, we explain a parallel implementation of VBGMM by GPGPU, and its evaluation. As a result, its execution speed is 524 times faster than that of CPU implementation.

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.