Heterogeneous clustering via adversarial deep Bayesian generative model

Xulun Ye, Jieyu Zhao

Abstract: This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first constructed to measure the similarity between different types of features. Then, a feature metric-restricted hierarchical sample generation process is established, in which sample with heterogeneous features is clustered by generating it from a similarity constraint hidden space. When estimating the model parameters and posterior probability, the corresponding variational inference algorithm is derived and implemented. To verify our model capability, we demonstrate our model on the synthetic dataset and show the superiority of the proposed method on some real datasets. Our source code is released on the website: Github.com/yexlwh/Heterogeneousclustering .