Multilinear Common Component Analysis via Kronecker Product Representation

• URL: http://arxiv.org/abs/2009.02695v2
• Date: Fri, 20 Nov 2020 08:06:05 GMT
• Title: Multilinear Common Component Analysis via Kronecker Product Representation
• Authors: Kohei Yoshikawa, Shuichi Kawano
• Abstract summary: We consider the problem of extracting a common structure from multiple tensor datasets. We propose multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance matrices. We develop an estimation algorithm for MCCA that guarantees mode-wise global convergence.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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