Multilinear Common Component Analysis via Kronecker Product Representation

@article{Yoshikawa2021MultilinearCC,
  title={Multilinear Common Component Analysis via Kronecker Product Representation},
  author={Kohei Yoshikawa and Shuichi Kawano},
  journal={Neural Computation},
  year={2021},
  volume={33},
  pages={2853-2880}
}
Abstract We consider the problem of extracting a common structure from multiple tensor data sets. 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 that lose little information of the multiple tensor data sets. We also develop an estimation algorithm for MCCA that guarantees mode-wise global convergence. Numerical… 

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