Multilinear common component analysis via Kronecker product representation

Kohei Yoshikawa, Shuichi Kawano

Research output: Contribution to journalLetterpeer-review

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.

Original languageEnglish
Pages (from-to)2853-2880
Number of pages28
JournalNeural Computation
Volume33
Issue number10
DOIs
Publication statusPublished - Sept 16 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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