基于Kronecker积表示的多线性公共成分分析
Multilinear Common Component Analysis via Kronecker Product Representation
AI总结：
本文提出多线性公共成分分析（MCCA），利用Kronecker积从多个张量数据集中提取公共结构，并开发了保证模态全局收敛的估计算法，数值实验验证了其有效性。
摘要：
我们考虑从多个张量数据集中提取公共结构的问题。为此，我们提出了基于模态协方差矩阵的Kronecker积的多线性公共成分分析（MCCA）。MCCA构建一个由原始变量线性组合表示的公共基，该基尽可能少地丢失多个张量数据集的信息。我们还开发了一种保证模态全局收敛的MCCA估计算法。数值研究证明了MCCA的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
