  Change-point detection (CPD) aims to locate abrupt transitions in the
generative model of a sequence of observations. When Bayesian methods are
considered, the standard practice is to infer the posterior distribution of the
change-point locations. However, for complex models (high-dimensional or
heterogeneous), it is not possible to perform reliable detection. To circumvent
this problem, we propose to use a hierarchical model, which yields observations
that belong to a lower-dimensional manifold. Concretely, we consider a
latent-class model with an unbounded number of categories, which is based on
the chinese-restaurant process (CRP). For this model we derive a continual
learning mechanism that is based on the sequential construction of the CRP and
the expectation-maximization (EM) algorithm with a stochastic maximization
step. Our results show that the proposed method is able to recursively infer
the number of underlying latent classes and perform CPD in a reliable manner.
