Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-mode Core Tensor Factorization based Low-Rankness and Its
  Applications to Tensor Completion   Low-rank tensor completion has been widely used in computer vision and
machine learning. This paper develops a novel multi-modal core tensor
factorization (MCTF) method combined with a tensor low-rankness measure and a
better nonconvex relaxation form of this measure (NC-MCTF). The proposed models
encode low-rank insights for general tensors provided by Tucker and T-SVD, and
thus are expected to simultaneously model spectral low-rankness in multiple
orientations and accurately restore the data of intrinsic low-rank structure
based on few observed entries. Furthermore, we study the MCTF and NC-MCTF
regularization minimization problem, and design an effective block successive
upper-bound minimization (BSUM) algorithm to solve them. This efficient solver
can extend MCTF to various tasks, such as tensor completion. A series of
experiments, including hyperspectral image (HSI), video and MRI completion,
confirm the superior performance of the proposed method.

17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga