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The notion of concept drift refers to the phenomenon that the data generatingdistribution changes over time; as a consequence machine learning models maybecome inaccurate and need adjustment. In this paper we consider the problem ofdetecting those change points in unsupervised learning. Many unsupervisedapproaches rely on the discrepancy between the sample distributions of two timewindows. This procedure is noisy for small windows, hence prone to induce falsepositives and not able to deal with more than one drift event in a window. Inthis paper we rely on structural properties of drift induced signals, which usespectral properties of kernel embedding of distributions. Based thereon wederive a new unsupervised drift detection algorithm, investigate itsmathematical properties, and demonstrate its usefulness in several experiments.

Precise Change Point Detection using Spectral Drift Detection