Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable interest in identifying specific structures within the novelty, and contamination in the known classes could completely blur the actual separation between manifest and new groups. Motivated by these problems, we propose a two-stage Bayesian semiparametric novelty detector, building upon prior information robustly extracted from a set of complete learning units. We devise a general-purpose multivariate methodology that we also extend to handle functional data objects. We provide insights on the model behavior by investigating the theoretical properties of the associated semiparametric prior. From the computational point of view we, propose, a suitable \(\varvec{\xi }\): \(\varvec{\xi }\)-sequence to construct an independent slice-efficient sampler that takes into account the difference between manifest and novelty components. We showcase our model performance through an extensive simulation study and applications on both multivariate and functional datasets, in which diverse and distinctive unknown patterns are discovered.

新颖性检测方法旨在将测试单元划分为已观察到的和先前未见的模式。但是，出现了两个重大问题：在新颖性中识别特定结构可能引起极大兴趣，并且已知类别中的污染可能会完全模糊清单和新类别之间的实际分离。受这些问题的影响，我们基于从一组完整的学习单元中稳健提取的先验信息，提出了一种两阶段贝叶斯半参数新颖性检测器。我们设计了一种通用的多元方法，我们还将其扩展为处理功能数据对象。我们通过研究关联的半参数先验的理论特性，提供关于模型行为的见解。从计算的角度来看，我们建议\（\ varvec {\ xi} \）：\（\ varvec {\ xi} \）-序列可构造一个独立的高效切片器，并考虑清单和新颖性组件之间的差异。我们通过在多变量和函数数据集上进行的广泛模拟研究和应用，展示了模型的性能，其中发现了多种独特的未知模式。