Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample-to-Sample Correspondence for Unsupervised Domain Adaptation   The assumption that training and testing samples are generated from the same
distribution does not always hold for real-world machine-learning applications.
The procedure of tackling this discrepancy between the training (source) and
testing (target) domains is known as domain adaptation. We propose an
unsupervised version of domain adaptation that considers the presence of only
unlabelled data in the target domain. Our approach centers on finding
correspondences between samples of each domain. The correspondences are
obtained by treating the source and target samples as graphs and using a convex
criterion to match them. The criteria used are first-order and second-order
similarities between the graphs as well as a class-based regularization. We
have also developed a computationally efficient routine for the convex
optimization, thus allowing the proposed method to be used widely. To verify
the effectiveness of the proposed method, computer simulations were conducted
on synthetic, image classification and sentiment classification datasets.
Results validated that the proposed local sample-to-sample matching method
out-performs traditional moment-matching methods and is competitive with
respect to current local domain-adaptation methods.

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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
2
The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th