Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multilinear Common Component Analysis via Kronecker Product
  Representation   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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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin