Component Splittingbased Approach for Multivariate Beta Mixture Models Learning Mixture models have become arguably one of the most widely used statistical approaches to perform inference on various types of data and have been successfully applied in data mining and numerous real world applications. In this work, we focus on the variational learning of finite multivariate Beta mixture models. Component splitting is one of the major assets of our model which prevents overfitting. Furthermore, the number of components can be estimated automatically and simultaneously along with parameters estimation. The performance and effectiveness of proposed model is verified by experimenting on three reallife applications, namely, cell image categorization, texture classification and object detection.

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.