Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-rank Tensor Grid for Image Completion   Tensor completion estimates missing components by exploiting the low-rank
structure of multi-way data. The recently proposed methods based on tensor
train (TT) and tensor ring (TR) show better performance in image recovery than
classical ones. Compared with TT and TR, the projected entangled pair state
(PEPS), which is also called tensor grid (TG), allows more interactions between
different dimensions, and may lead to more compact representation. In this
paper, we propose to perform image completion based on low-rank tensor grid. A
two-stage density matrix renormalization group algorithm is used for
initialization of TG decomposition, which consists of multiple TT
decompositions. The latent TG factors can be alternatively obtained by solving
alternating least squares problems. To further improve the computational
efficiency, a multi-linear matrix factorization for low rank TG completion is
developed by using parallel matrix factorization. Experimental results on
synthetic data and real-world images show the proposed methods outperform the
existing ones in terms of recovery accuracy.

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The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom