  A mixture of joint generalized hyperbolic distributions (MJGHD) is introduced
for asymmetric clustering for high-dimensional data. The MJGHD approach takes
into account the cluster-specific subspace, thereby limiting the number of
parameters to estimate while also facilitating visualization of results.
Identifiability is discussed, and a multi-cycle ECM algorithm is outlined for
parameter estimation. The MJGHD approach is illustrated on two real data sets,
where the Bayesian information criterion is used for model selection.
