Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a memoized online variational inference method that enables the "birth" and "merge" moves of clusters, allowing our framework to cluster data in a "dynamic-adaptive" manner, without requiring prior knowledge of the number of features. We name the framework as DIVA, a Dirichlet Process-based Incremental deep clustering framework via Variational Auto-Encoder. Our framework, which outperforms state-of-the-art baselines, exhibits superior performance in classifying complex data with dynamically changing features, particularly in the case of incremental features.

翻译：基于生成模型的深度聚类框架在复杂数据分类中表现优异，但由于需要预先知道聚类数量，在处理动态复杂特征时存在局限性。本文提出一种非参数深度聚类框架，采用无限高斯混合模型作为先验。该框架利用记忆化在线变分推断方法，支持聚类的"生成"与"合并"操作，使框架能够以"动态自适应"方式对数据进行聚类，无需预先知道特征数量。我们将该框架命名为DIVA——一种基于狄利克雷过程的变分自编码器增量式深度聚类框架。该框架在分类具有动态变化特征的复杂数据时展现出优越性能，特别是在增量特征场景下，其表现超越了现有的最先进基准方法。