  Low rank tensor completion is a well studied problem and has applications in
various fields. However, in many real world applications the data is dynamic,
i.e., new data arrives at different time intervals. As a result, the tensors
used to represent the data grow in size. Besides the tensors, in many real
world scenarios, side information is also available in the form of matrices
which also grow in size with time. The problem of predicting missing values in
the dynamically growing tensor is called dynamic tensor completion. Most of the
previous work in dynamic tensor completion make an assumption that the tensor
grows only in one mode. To the best of our Knowledge, there is no previous work
which incorporates side information with dynamic tensor completion. We bridge
this gap in this paper by proposing a dynamic tensor completion framework
called Side Information infused Incremental Tensor Analysis (SIITA), which
incorporates side information and works for general incremental tensors. We
also show how non-negative constraints can be incorporated with SIITA, which is
essential for mining interpretable latent clusters. We carry out extensive
experiments on multiple real world datasets to demonstrate the effectiveness of
SIITA in various different settings.
