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Title: Multiscale geometric feature-extraction for high-dimensional and non-euclidean data Authors:  Wolfgang Polonik - University of California at Davis (United States) [presenting]
Gabriel Chandler - Pomona College (United States)
Abstract: A method for extracting multiscale geometric features from a data cloud is presented. Each pair of data points is mapped into a real-valued feature function, whose construction is based on geometric considerations. The collection of these feature functions is then being used for further data analysis. Applications include classification, anomaly detection and data visualization. In contrast to the popular kernel trick, the construction of the feature functions is based on geometric considerations. The performance of the methodology is illustrated through applications to real data sets, and some theoretical guarantees are presented.