Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Iterative Block Tensor Singular Value Thresholding for Extraction of Low
  Rank Component of Image Data   Tensor principal component analysis (TPCA) is a multi-linear extension of
principal component analysis which converts a set of correlated measurements
into several principal components. In this paper, we propose a new robust TPCA
method to extract the princi- pal components of the multi-way data based on
tensor singular value decomposition. The tensor is split into a number of
blocks of the same size. The low rank component of each block tensor is
extracted using iterative tensor singular value thresholding method. The prin-
cipal components of the multi-way data are the concatenation of all the low
rank components of all the block tensors. We give the block tensor incoherence
conditions to guarantee the successful decom- position. This factorization has
similar optimality properties to that of low rank matrix derived from singular
value decomposition. Ex- perimentally, we demonstrate its effectiveness in two
applications, including motion separation for surveillance videos and
illumination normalization for face images.

22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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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: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th