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p-Laplacian Regularization for Indoor Scene Recognition
Weifeng Liu1; Xueqi Ma1; Yicong Zhou2; Dapeng Tao3; Jun Cheng4,5
Source PublicationIEEE Transactions on Cybernetics
Pages1 - 14

The explosive growth of multimedia data on the Internet makes it essential to develop innovative machine learning algorithms for practical applications especially where only a small number of labeled samples are available. Manifold regularized semi-supervised learning (MRSSL) thus received intensive attention recently because it successfully exploits the local structure of data distribution including both labeled and unlabeled samples to leverage the generalization ability of a learning model. Although there are many representative works in MRSSL, including Laplacian regularization (LapR) and Hessian regularization, how to explore and exploit the local geometry of data manifold is still a challenging problem. In this paper, we introduce a fully efficient approximation algorithm of graph p-Laplacian, which significantly saving the computing cost. And then we propose p-LapR (pLapR) to preserve the local geometry. Specifically, p-Laplacian is a natural generalization of the standard graph Laplacian and provides convincing theoretical evidence to better preserve the local structure. We apply pLapR to support vector machines and kernel least squares and conduct the implementations for scene recognition. Extensive experiments on the Scene 67 dataset, Scene 15 dataset, and UC-Merced dataset validate the effectiveness of pLapR in comparison to the conventional manifold regularization method

KeywordLaplacian Regularization (Lapr) Manifold Learning P-laplacian Scene Recognition Semi-supervised Learning (Ssl)
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Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorWeifeng Liu; Yicong Zhou; Dapeng Tao; Jun Cheng
Affiliation1.College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
2.Faculty of Science and Technology, University of Macau, Macau 999078, China
3.School of Information Science and Engineering, Yunnan University, Kunming 650091, China
4.Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
5.Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Weifeng Liu,Xueqi Ma,Yicong Zhou,et al. p-Laplacian Regularization for Indoor Scene Recognition[J]. IEEE Transactions on Cybernetics,2018:1 - 14.
APA Weifeng Liu,Xueqi Ma,Yicong Zhou,Dapeng Tao,&Jun Cheng.(2018).p-Laplacian Regularization for Indoor Scene Recognition.IEEE Transactions on Cybernetics,1 - 14.
MLA Weifeng Liu,et al."p-Laplacian Regularization for Indoor Scene Recognition".IEEE Transactions on Cybernetics (2018):1 - 14.
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