Abstract In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.

中文翻译：

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用于水上无人机状况评估的子空间聚类：状态模型改进的敏感性分析

摘要 在本文中，我们建议使用子空间聚类从观测序列中检测动力系统的状态。特别是，我们生成了稀疏且可解释的模型，这些模型将参与自主水域监测的水上无人机的状态与无人机传感器收集的数据的属性（例如，统计分布）相关联。使用的子空间聚类算法称为 SubCMedians。进行定量实验分析以研究 i) 学习参数和性能之间的联系，ii) 数据和性能中的噪声。通过该分析获得的聚类优于以前的方法产生的聚类。