文章基本信息

标题：Document Classification based on Optimal Laprls
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作者：Wang, Ziqiang ; Sun, Xia ; Zhang, Lijie 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2013
卷号：8
期号：4
页码：1011-1018
DOI：10.4304/jsw.8.4.1011-1018
语种：English
出版社：Academy Publisher
摘要：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 l ¹-norm optimization problem; then the normalized graph Laplacian is derived for revealing the intrinsic document manifold structure; finally, the Nyström method-based low-rank 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.
关键词：document classification;semi-supervised learning;optimal Laplacian regularized least square (OLapRLS);kernel low-rank approximation