文件名称：数据流形的距离和几何结构
文件大小：5.14MB
文件格式：PDF
更新时间：2021-12-03 17:19:58
机器学习
根据信息几何的观点，介绍了数据流形的距离。 The increasing importance of data in the modern world has created a need for new mathematical techniques to analyze this data. We explore and develop the use of geometry—specifically differential geometry—as a means for such analysis, in two parts. First, we provide a general framework to discover patterns contained in time series data using a geometric framework of assigning distance, clustering, and then forecasting. Second, we attempt to define a Riemannian metric on the space containing the data in order to introduce a notion of distance intrinsic to the data, providing a novel way to probe the data for insight.