  Novelty detection methods aim at partitioning the test units into already
observed and previously unseen patterns. However, two significant issues arise:
there may be considerable interest in identifying specific structures within
the novelty, and contamination in the known classes could completely blur the
actual separation between manifest and new groups. Motivated by these problems,
we propose a two-stage Bayesian semiparametric novelty detector, building upon
prior information robustly extracted from a set of complete learning units. We
devise a general-purpose multivariate methodology that we also extend to handle
functional data objects. We provide insights on the model behavior by
investigating the theoretical properties of the associated semiparametric
prior. From the computational point of view, we propose a suitable
$\boldsymbol{\xi}$-sequence to construct an independent slice-efficient sampler
that takes into account the difference between manifest and novelty components.
We showcase our model performance through an extensive simulation study and
applications on both multivariate and functional datasets, in which diverse and
distinctive unknown patterns are discovered.
