  Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and
analysis of multiple data sources and helps discover meaningful information.
Nonnegative CMTF (N-CMTF) has been employed in many applications for
identifying latent patterns, prediction, and recommendation. However, due to
the added complexity with coupling between tensor and matrix data, existing
N-CMTF algorithms exhibit poor computation efficiency. In this paper, a
computationally efficient N-CMTF factorization algorithm is presented based on
the column-wise element selection, preventing frequent gradient updates.
Theoretical and empirical analyses show that the proposed N-CMTF factorization
algorithm is not only more accurate but also more computationally efficient
than existing algorithms in approximating the tensor as well as in identifying
the underlying nature of factors.
