论文标题
柱状元素选择用于计算有效的非负耦合矩阵张量分解
Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization
论文作者
论文摘要
耦合矩阵张量分解（CMTF）促进了多个数据源的集成和分析，并有助于发现有意义的信息。非负CMTF（N-CMTF）已在许多应用中用于识别潜在模式，预测和建议。但是，由于张量和矩阵数据之间的耦合增加了复杂性，现有的N-CMTF算法的计算效率较差。在本文中，根据列元元素的选择，介绍了计算有效的N-CMTF分解算法，以防止频繁的梯度更新。理论和经验分析表明，所提出的N-CMTF分解算法不仅比现有算法更准确，而且在近似张量和识别基本因素的性质方面比现有算法更有效。
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.
