  Change point detection algorithms have numerous applications in fields of
scientific and economic importance. We consider the problem of change point
detection on compositional multivariate data (each sample is a probability mass
function), which is a practically important sub-class of general multivariate
data. While the problem of change-point detection is well studied in univariate
setting, and there are few viable implementations for a general multivariate
data, the existing methods do not perform well on compositional data. In this
paper, we propose a parametric approach for change point detection in
compositional data. Moreover, using simple transformations on data, we extend
our approach to handle any general multivariate data. Experimentally, we show
that our method performs significantly better on compositional data and is
competitive on general data compared to the available state of the art
implementations.
