Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Columnwise Element Selection for Computationally Efficient Nonnegative
  Coupled Matrix Tensor Factorization   Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and
analysis of multiple data sources and helps discover meaningful information.
Nonnegative CMTF (N-CMTF) has been employed in many applications for
identifying latent patterns, prediction, and recommendation. However, due to
the added complexity with coupling between tensor and matrix data, existing
N-CMTF algorithms exhibit poor computation efficiency. In this paper, a
computationally efficient N-CMTF factorization algorithm is presented based on
the column-wise element selection, preventing frequent gradient updates.
Theoretical and empirical analyses show that the proposed N-CMTF factorization
algorithm is not only more accurate but also more computationally efficient
than existing algorithms in approximating the tensor as well as in identifying
the underlying nature of factors.

36
The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio