Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization
This work addresses computational bottlenecks for researchers and practitioners using N-CMTF in applications like data integration and pattern discovery, representing an incremental improvement.
The paper tackled the poor computational efficiency of existing Nonnegative Coupled Matrix Tensor Factorization (N-CMTF) algorithms by proposing a new algorithm based on column-wise element selection to prevent frequent gradient updates, resulting in improved accuracy and computational efficiency compared to existing methods.
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.