论文标题
一个两阶段的贝叶斯半参数模型，用于使用可靠的先验信息来检测新颖性
A Two-Stage Bayesian Semiparametric Model for Novelty Detection with Robust Prior Information
论文作者
论文摘要
新颖的检测方法旨在将测试单元分配到已经观察到的和以前看不见的模式中。但是，出现了两个重要的问题：在识别新颖性内的特定结构中可能有很大的兴趣，并且已知类别中的污染可能会完全模糊清单和新群体之间的实际分离。在这些问题的激励下，我们提出了一个两个阶段的贝叶斯半参数探测器，这是基于从一组完整的学习单元中鲁棒提取的先前信息。我们设计了一种通用多元方法，我们还扩展了以处理功能数据对象。我们通过研究相关半参数先验的理论特性来提供有关模型行为的见解。从计算的角度来看，我们提出了一个合适的$ \boldsymbolξ$序列，以构建一个独立的切片采样器，该采样器考虑了明显和新颖性成分之间的差异。我们通过广泛的模拟研究和对多元功能数据集的应用来展示我们的模型性能，其中发现了多种多样的未知模式。
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 $\boldsymbolξ$-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.
