  We consider the problem of detecting abrupt changes in the distribution of a
multi-dimensional time series, with limited computing power and memory. In this
paper, we propose a new, simple method for model-free online change-point
detection that relies only on fast and light recursive statistics, inspired by
the classical Exponential Weighted Moving Average algorithm (EWMA). The
proposed idea is to compute two EWMA statistics on the stream of data with
different forgetting factors, and to compare them. By doing so, we show that we
implicitly compare recent samples with older ones, without the need to
explicitly store them. Additionally, we leverage Random Features (RFs) to
efficiently use the Maximum Mean Discrepancy as a distance between
distributions, furthermore exploiting recent optical hardware to compute
high-dimensional RFs in near constant time. We show that our method is
significantly faster than usual non-parametric methods for a given accuracy.

Machine Learning;Machine Learning