  Streaming tensor factorization is a powerful tool for processing high-volume
and multi-way temporal data in Internet networks, recommender systems and
image/video data analysis. Existing streaming tensor factorization algorithms
rely on least-squares data fitting and they do not possess a mechanism for
tensor rank determination. This leaves them susceptible to outliers and
vulnerable to over-fitting. This paper presents a Bayesian robust streaming
tensor factorization model to identify sparse outliers, automatically determine
the underlying tensor rank and accurately fit low-rank structure. We implement
our model in Matlab and compare it with existing algorithms on tensor datasets
generated from dynamic MRI and Internet traffic.
