The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

中文翻译：

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简约高斯混合模型和场景聚类的在线非参数贝叶斯分析。

混合模型是聚类分析中非常强大且灵活的工具。基于Dirichlet过程和简约高斯分布，我们提出了一个新的非参数混合框架来解决具有挑战性的聚类问题。同时，模型的推论依赖于有效的在线变分贝叶斯方法，该方法在一定程度上增强了整体与零件之间的信息交换，并适用于可扩展的数据集。现场数据库上的实验表明，新颖的聚类框架与卷积神经网络结合以进行特征提取时，具有比其他模型有意义的优势。