Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vector Quantization by Minimizing Kullback-Leibler Divergence   This paper proposes a new method for vector quantization by minimizing the
Kullback-Leibler Divergence between the class label distributions over the
quantization inputs, which are original vectors, and the output, which is the
quantization subsets of the vector set. In this way, the vector quantization
output can keep as much information of the class label as possible. An
objective function is constructed and we also developed an iterative algorithm
to minimize it. The new method is evaluated on bag-of-features based image
classification problem.

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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
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The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
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The categories of this paper are: eess
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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: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con