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Updated: Oct 27, 2025

Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Multilinear Common Component Analysis via Kronecker Product Representation
Kohei Yoshikawa1, Shuichi Kawano2
1Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu-shi, Tokyo 182-8585, Japan yoshikawa.kohei615@gmail.com.
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 studies are conducted to show the effectiveness of MCCA.
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