Multidimensional sensors represent an increasingly popular, yet challenging data source in modern statistics. Using tools from the emerging branch of Topological Data Analysis (TDA), we address two issues frequently encountered when analysing sensor data, namely their (often) high dimension and their sensibility to the reference system. We show how topological invariants provide a tool for detecting change--points which is robust with respect to both the time resolution we consider and the sensor placement.
Multiresolution topological data analysis for robust activity tracking / Trappolini, Giovanni; Padellini, Tullia; Brutti, Pierpaolo. - (2019), pp. 1119-1124. (Intervento presentato al convegno Smart Statistics for Smart Applications - Italian Statistical Society SIS 2019 tenutosi a Milano - Università Cattolica del Sacro Cuore).
Multiresolution topological data analysis for robust activity tracking
Giovanni Trappolini;Tullia Padellini;Pierpaolo Brutti
2019
Abstract
Multidimensional sensors represent an increasingly popular, yet challenging data source in modern statistics. Using tools from the emerging branch of Topological Data Analysis (TDA), we address two issues frequently encountered when analysing sensor data, namely their (often) high dimension and their sensibility to the reference system. We show how topological invariants provide a tool for detecting change--points which is robust with respect to both the time resolution we consider and the sensor placement.| File | Dimensione | Formato | |
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