  We propose a novel algorithm for the computation of canonical polyadic
decomposition (CPD) of large-scale tensors. The proposed algorithm generalizes
the random projection (RAP) technique, which is often used to compute
large-scale decompositions, from one single projection to multiple but coupled
random projections (CoRAP). The proposed CoRAP technique yields a set of
tensors that together admits a coupled CPD (C-CPD) and a C-CPD algorithm is
then used to jointly decompose these tensors. The results of C-CPD are finally
fused to obtain factor matrices of the original large-scale data tensor. As
more data samples are jointly exploited via C-CPD, the proposed CoRAP based CPD
is more accurate than RAP based CPD. Experiments are provided to illustrate the
performance of the proposed approach.
