Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Technical Report: Band selection for nonlinear unmixing of hyperspectral
  images as a maximal clique problem   Kernel-based nonlinear mixing models have been applied to unmix spectral
information of hyperspectral images when the type of mixing occurring in the
scene is too complex or unknown. Such methods, however, usually require the
inversion of matrices of sizes equal to the number of spectral bands. Reducing
the computational load of these methods remains a challenge in large scale
applications. This paper proposes a centralized method for band selection (BS)
in the reproducing kernel Hilbert space (RKHS). It is based upon the coherence
criterion, which sets the largest value allowed for correlations between the
basis kernel functions characterizing the unmixing model. We show that the
proposed BS approach is equivalent to solving a maximum clique problem (MCP),
that is, searching for the biggest complete subgraph in a graph. Furthermore,
we devise a strategy for selecting the coherence threshold and the Gaussian
kernel bandwidth using coherence bounds for linearly independent bases.
Simulation results illustrate the efficiency of the proposed method.

35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin