In preprocessing tensor-valued data, e.g. images and videos, a common
procedure is to vectorize the observations and subject the resulting vectors to
one of the many methods used for independent component analysis (ICA). However,
the tensor structure of the original data is lost in the vectorization and, as
a more suitable alternative, we propose the matrix- and tensor fourth order
blind identification (MFOBI and TFOBI). In these tensorial extensions of the
classic fourth order blind identification (FOBI) we assume a Kronecker
structure for the mixing and perform FOBI simultaneously on each direction of
the observed tensors. We discuss the theory and assumptions behind MFOBI and
TFOBI and provide two different algorithms and related estimates of the
unmixing matrices along with their asymptotic properties. Finally, simulations
are used to compare the method's performance with that of classical FOBI for
vectorized data and we end with a real data clustering example.Comment: 26 pages, 4 figure