Volume 8 Number 4 (Apr. 2013)
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JSW 2013 Vol.8(4): 1011-1018 ISSN: 1796-217X
doi: 10.4304/jsw.8.4.1011-1018

Document Classification based on Optimal LapRLS

Ziqiang Wang1, Xia Sun1, Lijie Zhang2, Xu Qian2
1Henan University of Technology, Zhengzhou, 450001, China
2China University of Mining and Technology (Beijing), Beijing, 100083, China


AbstractTo effectively utilize a large number of unlabeled data and a small part of labeled data in the document classification problem, a novel semi-supervised learning algorithm called optimal Laplacian regularized least square (OLapRLS) is proposed in this paper. This algorithm first obtains the data-adaptive edge weights by solving the l1- norm optimization problem; then the normalized graph Laplacian is derived for revealing the intrinsic document manifold structure; finally, the Nyström method-based lowrank approximation method is adopted to reduce the computational complexity in manipulating the large kernel matrix. Experimental results on three well-known document datasets demonstrate the effectiveness and efficiency of the proposed OLapRLS algorithm.

Index TermsDocument classification, semi-supervised learning, optimal Laplacian regularized least square (OLapRLS), kernel low-rank approximation.

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Cite: Ziqiang Wang, Xia Sun, Lijie Zhang, Xu Qian, "Document Classification based on Optimal LapRLS," Journal of Software vol. 8, no. 4, pp. 1011-1018, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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