Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor Recovery Based on Tensor Equivalent Minimax-Concave Penalty   Tensor recovery is an important problem in computer vision and machine
learning. It usually uses the convex relaxation of tensor rank and $l_{0}$
norm, i.e., the nuclear norm and $l_{1}$ norm respectively, to solve the
problem. It is well known that convex approximations produce biased estimators.
In order to overcome this problem, a corresponding non-convex regularizer has
been proposed to solve it. Inspired by matrix equivalent Minimax-Concave
Penalty (EMCP), we propose and prove theorems of tensor equivalent
Minimax-Concave Penalty (TEMCP). The tensor equivalent MCP (TEMCP) as a
non-convex regularizer and the equivalent weighted tensor $\gamma$ norm (EWTGN)
which can represent the low-rank part are obtained. Both of them can realize
weight adaptive. At the same time, we propose two corresponding adaptive models
for two classical tensor recovery problems, low-rank tensor completion (LRTC)
and tensor robust principal component analysis (TRPCA), and the optimization
algorithm is based on alternating direction multiplier (ADMM). This novel
iterative adaptive algorithm can produce more accurate tensor recovery effect.
For the tensor completion model, multispectral image (MSI), magnetic resonance
imaging (MRI) and color video (CV) data sets are considered, while for the
tensor robust principal component analysis model, hyperspectral image (HSI)
denoising under gaussian noise plus salt and pepper noise is considered. The
proposed algorithm is superior to the state-of-arts method, and the algorithm
is guaranteed to meet the reduction and convergence through experiments.

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The categories of this paper are: math
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
17
The categories of this paper are: q-fin