  Consider a data set collected by (individuals-features) pairs in different
times. It can be represented as a tensor of three dimensions (Individuals,
features and times). The tensor biclustering problem computes a subset of
individuals and a subset of features whose signal trajectories over time lie in
a low-dimensional subspace, modeling similarity among the signal trajectories
while allowing different scalings across different individuals or different
features. This approach are based on spectral decomposition in order to build
the desired biclusters. We evaluate the quality of the results from each
algorithms with both synthetic and real data set.
