A0150
Title: Multivariate outlier detection With ICS (Part 1)
Authors: Anne Ruiz-Gazen - University Toulouse 1 Capitole (France) [presenting]
Abstract: After a practical introduction of the general use of R for multivariate data analysis,the objective of the course is to present the Invariant Coordinate Selection (ICS) method as a tool for multivariate outlier detection. ICS was proposed in 2009 and shows remarkable properties for revealing data structures such as outliers or clusters. It is based on the simultaneous spectral decomposition of two scatter matrices and leads to an ane invariant coordinate system where the Euclidian distance corresponds to a Mahalanobis Distance (MD) in the original system. However, unlike MD, ICS makes it possible to select relevant components. This proves useful for detecting outliers lying in a small dimensional subspace for data sets in large dimensions. This context appears in particular in high reliability standards elds such as automotive, avionics or aerospace. In this context, ICS can be useful for detecting anomalies with a small proportion of false positives. The method will be illustrated on several artificial and real data sets using the recent R packages ICSOutlier and ICSShiny. The package ICSOutlier allows to choose scatter matrices, automatically select the most relevant components, calculate an outlierness index and identify potential outlying observations. The ICSShiny package provides a user-friendly application for ICS in particular for outlier detection.
