Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fusion of heterogeneous bands and kernels in hyperspectral image
  processing   Hyperspectral imaging is a powerful technology that is plagued by large
dimensionality. Herein, we explore a way to combat that hindrance via
non-contiguous and contiguous (simpler to realize sensor) band grouping for
dimensionality reduction. Our approach is different in the respect that it is
flexible and it follows a well-studied process of visual clustering in
high-dimensional spaces. Specifically, we extend the improved visual assessment
of cluster tendency and clustering in ordered dissimilarity data unsupervised
clustering algorithms for supervised hyperspectral learning. In addition, we
propose a way to extract diverse features via the use of different proximity
metrics (ways to measure the similarity between bands) and kernel functions.
The discovered features are fused with $l_{\infty}$-norm multiple kernel
learning. Experiments are conducted on two benchmark datasets and our results
are compared to related work. These datasets indicate that contiguous or not is
application specific, but heterogeneous features and kernels usually lead to
performance gain.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org