  This paper tackles the problem of robust covariance matrix estimation when
the data is incomplete. Classical statistical estimation methodologies are
usually built upon the Gaussian assumption, whereas existing robust estimation
ones assume unstructured signal models. The former can be inaccurate in
real-world data sets in which heterogeneity causes heavy-tail distributions,
while the latter does not profit from the usual low-rank structure of the
signal. Taking advantage of both worlds, a covariance matrix estimation
procedure is designed on a robust (mixture of scaled Gaussian) low-rank model
by leveraging the observed-data likelihood function within an
expectation-maximization algorithm. It is also designed to handle general
pattern of missing values. The proposed procedure is first validated on
simulated data sets. Then, its interest for classification and clustering
applications is assessed on two real data sets with missing values, which
include multispectral and hyperspectral time series.
