¹Henan University of Technology, Zhengzhou, 450001, China
²China University of Mining and Technology (Beijing), Beijing, 100083, China

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Abstract—To 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 Terms—Document 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.