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Machine learning for multivariate and functional anomaly detection: ordering and data depth
This event is organized with the participation of Pavlo Mozharovskyi, Telecom Paris.
datacraft members only
Anomaly detection (Chandola et al., 2009) is a branch of machine learning which aims at identifying observations that exhibit abnormal behavior. Be it measurement errors, disease development, severe weather, production quality default(s) (items) or failed equipment, financial frauds or crisis events, their on-time identification, isolation and explanation constitute an important task in almost any branch of industry and science.
During this workshop, you will discuss the concept of data depth in both functional and multivariate settings, review most common notion of the depth function (halfspace (Tukey, 1975), projection (Zuo & Sefling,2000), zonoid (Mosler, 2002), spatial depth (Koltchinskii, 1997); integrated (Claeskens et al., 2014) and curve (Lafaye De Micheaux et al., 2020) functional depths, functional isolation forest Staerman et al. (2019), and focus on a number of real-world applications ranging from simulated situations to hurricane tracks and brain imaging.