  The notion of concept drift refers to the phenomenon that the data generating
distribution changes over time; as a consequence machine learning models may
become inaccurate and need adjustment. In this paper we consider the problem of
detecting those change points in unsupervised learning. Many unsupervised
approaches rely on the discrepancy between the sample distributions of two time
windows. This procedure is noisy for small windows, hence prone to induce false
positives and not able to deal with more than one drift event in a window. In
this paper we rely on structural properties of drift induced signals, which use
spectral properties of kernel embedding of distributions. Based thereon we
derive a new unsupervised drift detection algorithm, investigate its
mathematical properties, and demonstrate its usefulness in several experiments.
