Mathematical Methods to Extract Information from High-dimensional Data (E01)

从高维数据中提取信息的数学方法（E01）

• 批准号: 437179582
• 金额: --
• 依托单位: Professur Angewandte Funktionalanalysis
• 依托单位国家: 德国
• 项目类别: Collaborative Research Centres
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/437179582?language=en
• 关键词: Mathematical; Methods; Extract; Information; dimensional

With the aim to develop a general mathematical methodology for identifying events in high dimensional time series, the work program combines three different approximation methods. These are based on an analytic numerical approach for function approximation and dimension reduction, a stochastic approach for statistical clustering and a discrete graph-based method for generating a finite state transition graph model. In bringing these techniques together, the goal is to come up with new algorithmic methods that answer both theoretical and practical questions and support human-interpretable time series analysis.

为了开发一种通用的数学方法来识别高维时间序列中的事件，工作程序结合了三种不同的近似方法。这些方法基于函数逼近和降维的解析数值方法，统计聚类的随机方法和生成有限状态转移图模型的基于离散图的方法。将这些技术结合在一起，目标是提出新的算法方法来回答理论和实践问题，并支持人类可解释的时间序列分析。