A taxonomy of outlier detection methods for robust classification in multivariate functional data
Wednesday 6th April 2016
Ieva, F.; Paganoni, A.M.
We propose a new method for robust classification of multivariate functional data. We exploit the joint use of two different depth measures, generalizing the outliergram to the multivariate functional framework, aiming at detecting and discarding both shape and magnitude outliers in order to robustify the reference samples of data, composed by G different known groups. We asses by means of a simulation study method’s performance in comparison with different outlier detection methods. Finally we consider a real dataset: we classify a data minimizing a suitable distance from the center of reference groups. We compare performance of supervised classification on test sets training the algorithm on original dataset and on the robustified one, respectively.