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Column-Wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Sep2021, Vol. 33 Issue 9, p3173-3186, 14p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 33
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 153128124
- Full Text :
- https://doi.org/10.1109/TKDE.2020.2967045