论文标题
通过kronecker产品表示的多线性共同组件分析
Multilinear Common Component Analysis via Kronecker Product Representation
论文作者
论文摘要
我们考虑从多个张量数据集中提取共同结构的问题。为此，我们根据模式协方差矩阵的Kronecker产品提出了多线性共同组件分析（MCCA）。 MCCA构建了一个以原始变量的线性组合表示的共同基础，该组合的损失很少，因为多个张量数据集的信息很少。我们还为MCCA开发了一种估计算法，以保证模式的全球融合。进行数值研究以显示MCCA的有效性。
We consider the problem of extracting a common structure from multiple tensor datasets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance matrices. MCCA constructs a common basis represented by linear combinations of the original variables which loses as little information of the multiple tensor datasets. We also develop an estimation algorithm for MCCA that guarantees mode-wise global convergence. Numerical studies are conducted to show the effectiveness of MCCA.
