NDLI: Label transfer and propagation for domain adaptation

Content Provider	IEEE Xplore Digital Library
Author	Shuang Li Lei Zhu Gao Huang Shiji Song
Copyright Year	2015
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Shuang Li; Gao Huang; Shiji Song) \|\| China Ocean Miner. Resources R&D Assoc., Beijing, China (Lei Zhu)
Abstract	Traditional classification algorithms often perform well when training and testing data are drawn from the identical distribution. However, in real applications, this condition may be not satisfied. Domain adaptation is an effective approach to deal with this problem. In this paper, we propose an efficient two-stage algorithm for domain adaptation. In the label transfer stage, we utilize training classifier to predict testing data with different weights (confidence) based on their signed distance to the domain separator, which is a classifier maximally separating training data (from source domain) and testing data (from target domain) apart. In the label propagation stage, we introduce manifold regularization to propagate the labels of target data with larger weights to ones with smaller weights. Furthermore, the target classifier can be obtained in a closed form. The extensive experiments on an artificial dataset and a real benchmark verify the effectiveness of our approach. Empirical results demonstrate that the proposed method is competitive with state-of-the-art domain adaptation algorithms.
Starting Page	50
Ending Page	54
File Size	908867
Page Count	5
File Format	PDF
e-ISBN	9781479964185
DOI	10.1109/ICEDIF.2015.7280161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Support vector machines Training Accuracy Particle separators Prediction algorithms Testing
Content Type	Text
Resource Type	Article

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