  We propose a novel procedure for outlier detection in functional data, in a
semi-supervised framework. As the data is functional, we consider the
coefficients obtained after projecting the observations onto orthonormal bases
(wavelet, PCA). A multiple testing procedure based on the two-sample test is
defined in order to highlight the levels of the coefficients on which the
outliers appear as significantly different to the normal data. The selected
coefficients are then called features for the outlier detection, on which we
compute the Local Outlier Factor to highlight the outliers. This procedure to
select the features is applied on simulated data that mimic the behaviour of
space telemetries, and compared with existing dimension reduction techniques.
