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Joint Subspace Learning and Sparse Regression for Feature Selection in Kernel Space

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Applications and Techniques in Information Security (ATIS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 950))

Abstract

In this paper, we propose a novel feature selection method to jointly map original data to kernel space and conduct both subspace learning (via locality preserving projection) and feature selection (via a sparsity constraint). The kernel method is used to explore the nonlinear relationship between data and subspace learning is used to maintain the local structure of the data. As a result, we eliminate redundant and irrelevant features and thus make our method select a large amount of informative and distinguishing features. By comparing our proposed method with some state-of-the-art methods, the experimental results showed that the proposed method outperformed the comparisons in terms of clustering task.

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Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Guangxi Teachers Education University, Nanning, 530299, China

    Long Chen

  2. College of Continue Education, Guangxi Teachers Education University, Nanning, 530299, China

    Zhi Zhong

Authors
  1. Long Chen
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  2. Zhi Zhong
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Corresponding author

Correspondence to Zhi Zhong .

Editor information

Editors and Affiliations

  1. Guangxi University, Nanning, China

    Qingfeng Chen

  2. Macquarie University, Sydney, NSW, Australia

    Jia Wu

  3. Guangxi Normal University, Nanning, China

    Shichao Zhang

  4. Guangxi College of Education, Nanning, China

    Changan Yuan

  5. Deakin University, Geelong, VIC, Australia

    Lynn Batten

  6. Deakin University, Geelong, VIC, Australia

    Gang Li

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© 2018 Springer Nature Singapore Pte Ltd.

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Chen, L., Zhong, Z. (2018). Joint Subspace Learning and Sparse Regression for Feature Selection in Kernel Space. In: Chen, Q., Wu, J., Zhang, S., Yuan, C., Batten, L., Li, G. (eds) Applications and Techniques in Information Security. ATIS 2018. Communications in Computer and Information Science, vol 950. Springer, Singapore. https://doi.org/10.1007/978-981-13-2907-4_13

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  • DOI: https://doi.org/10.1007/978-981-13-2907-4_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2906-7

  • Online ISBN: 978-981-13-2907-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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