  CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with
multi-way data. For real-time or large-scale tensors, based on the ideas of
randomized-sampling CP decomposition algorithm and online CP decomposition
algorithm, a novel CP decomposition algorithm called randomized online CP
decomposition (ROCP) is proposed in this paper. The proposed algorithm can
avoid forming full Khatri-Rao product, which leads to boost the speed largely
and reduce memory usage. The experimental results on synthetic data and
real-world data show the ROCP algorithm is able to cope with CP decomposition
for large-scale tensors with arbitrary number of dimensions. In addition, ROCP
can reduce the computing time and memory usage dramatically, especially for
large-scale tensors.
