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Ensemble p-Laplacian Regularization for Scene Image Recognition

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Abstract

Recently, manifold regularized semi-supervised learning (MRSSL) received considerable attention, because it successfully exploits the geometry of the intrinsic data probability distribution to leverage the performance of a learning model. As a natural nonlinear generalization of graph Laplacian, p-Laplacian has been proved having the rich theoretical foundations to better preserve the local structure. However, it is difficult to determine the fitting graph p-Lapalcian, i.e., the parameter p, which is a critical factor for the performance of graph p-Laplacian. Therefore, we develop an ensemble p-Laplacian regularization (EpLapR) to fully approximate the intrinsic manifold of the data distribution. EpLapR incorporates multiple graphs into a regularization term in order to sufficiently explore the complementation of graph p-Laplacian. Specifically, we construct a fused graph by introducing an optimization approach to assign suitable weights on different p value graphs. And then, we conduct semi-supervised learning framework on the fused graph. Extensive experiments on UC-Merced dataset and Scene 15 dataset demonstrate the effectiveness and efficiency of the proposed method.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61671480, in part by the Foundation of Shandong Province under Grant ZR2018MF017, the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) under Grant 18CX07011A and Grant YCX2017059, the National Natural Science Foundation of China under Grant 61772455 and Grant U1713213, the Yunnan Natural Science Funds under Grant 2016FB105, the Program for Excellent Young Talents of Yunnan University under Grant WX069051, the Macau Science and Technology Development Fund under Grant FDCT/189/2017/A3, and the Research Committee at University of Macau under Grant MYRG2016-00123-FST and Grant MYRG2018-00136-FST.

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Authors and Affiliations

  1. China University of Petroleum (East China), Qingdao, 266580, China

    Xueqi Ma & Weifeng Liu

  2. Yunnan University, Kunming, 650091, China

    Dapeng Tao

  3. Faculty of Science and Technology, University of Macau, Macau, China

    Yicong Zhou

Authors
  1. Xueqi Ma
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  2. Weifeng Liu
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  3. Dapeng Tao
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  4. Yicong Zhou
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Correspondence to Weifeng Liu.

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Ma, X., Liu, W., Tao, D. et al. Ensemble p-Laplacian Regularization for Scene Image Recognition. Cogn Comput 11, 841–854 (2019). https://doi.org/10.1007/s12559-019-09637-z

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  • DOI: https://doi.org/10.1007/s12559-019-09637-z

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