Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Further Theoretical Study of Distribution Separation Method for
  Information Retrieval   Recently, a Distribution Separation Method (DSM) is proposed for relevant
feedback in information retrieval, which aims to approximate the true relevance
distribution by separating a seed irrelevance distribution from the mixture
one. While DSM achieved a promising empirical performance, theoretical analysis
of DSM is still need further study and comparison with other relative retrieval
model. In this article, we first generalize DSM's theoretical property, by
proving that its minimum correlation assumption is equivalent to the maximum
(original and symmetrized) KL-Divergence assumption. Second, we also
analytically show that the EM algorithm in a well-known Mixture Model is
essentially a distribution separation process and can be simplified using the
linear separation algorithm in DSM. Some empirical results are also presented
to support our theoretical analysis.

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The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics