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Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning

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Advanced Data Mining and Applications (ADMA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10604))

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Abstract

In this paper we show that manifold learning could effectively find the essential dimension of nonlinear high-dimensional data, but it could not use class label information of the data because it is an unsupervised learning method. This paper explores a novel supervised feature selection algorithm based on low-rank and manifold learning. Specifically, we obtain the coefficient matrix according to the relationship between data and class label. Then we combine sparse learning and manifold learning to conduct feature selection. Finally, we use the low-rank representation to further adjust the result of feature selection. Experimental results show that our new method obtains the best results on the four public datasets when compared with six existing methods.

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Notes

  1. 1.

    http://www.csie.ntu.edu.tw/cjlin/libsvm/.

  2. 2.

    http://archive.ics.uci.edu/ml/.

  3. 3.

    http://featureselection.asu.edu/datasets.php.

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Acknowledgments

This work was supported in part by the China Key Research Program (Grant No: 2016YFB1000905), the China 973 Program (Grant No: 2013CB329404), the China 1000-Plan National Distinguished Professorship, the Nation Natural Science Foundation of China (Grants No: 61573270, 61672177, and 61363009), the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011), the Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents, the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing, the Research Fund of Guangxi Key Lab of MIMS (16-A-01-01 and 16-A-01-02), and the Guangxi Bagui Teams for Innovation and Research, and Innovation Project of Guangxi Graduate Education under grant YCSW2017065, XYCSZ2017064 and XYCSZ2017067.

Author information

Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security, Guilin, 541004, China

    Yue Fang, Shichao Zhang, Cong Lei & Xiaoyi Hu

  2. College of CS and IT, Guangxi Normal University, Guilin, 541004, China

    Yue Fang, Shichao Zhang, Cong Lei & Xiaoyi Hu

  3. College of Cyber Security, Jinan University, Guangzhou, 510632, China

    Jilian Zhang

Authors
  1. Yue Fang
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  2. Jilian Zhang
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  3. Shichao Zhang
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  4. Cong Lei
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  5. Xiaoyi Hu
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Corresponding author

Correspondence to Shichao Zhang .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Fang, Y., Zhang, J., Zhang, S., Lei, C., Hu, X. (2017). Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

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