This paper proposes an extension of Random Projection Depth (RPD) to cope with multiple modalities and non-convexity on data clouds. In the framework of the proposed method, the RPD is computed in a reproducing kernel Hilbert space. With the help of kernel principal component analysis, we expect that the proposed method can cope with the above multiple modalities and non-convexity. The experimental results demonstrate that the proposed method outperforms RPD and is comparable to other existing detection models on benchmark datasets regarding Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).

翻译：本文提出了随机投影深度(RPD)的扩展方法，以处理数据云中的多模态性和非凸性。在所提方法框架中，RPD在再生核希尔伯特空间中计算。借助核主成分分析，我们期望该方法能够应对上述多模态性和非凸性问题。实验结果表明，在基准数据集上，所提方法在受试者工作特征(ROC)曲线下面积(AUCs)指标上优于RPD，且与其他现有检测模型性能相当。