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Unsupervised Feature Selection Using Both Similar and Dissimilar Structures

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1961))

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Abstract

Unsupervised feature selection method is widely used to handle the rapid increasing complex and high-dimensional sparse data without labels. Many good methods have been proposed in which the relationships between the similar data points are mainly considered. The graph embedding theory is used which occupies a large proportion. Despite their achievements, the existing methods neglect the information from the most dissimilar data. In this paper, we follow the research line of graph embedding and present a novel method for unsupervised feature selection. Two different viewpoints in the positive and negative are used to keep the data structure after feature selection. Besides a Laplacian matrix by which the most similar data structure is kept, we build an additional Laplacian matrix to keep the least similar data structure. Furthermore, an efficient algorithm is designed by virtue of the existing generalized powered iteration method. Extensive experiments on six benchmark data sets are conducted to verify the state-of-the-art effectiveness and superiority of the proposed method.

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Notes

  1. 1.

    http://archive.ics.uci.edu/dataset/54/isolet.

  2. 2.

    https://jundongl.github.io/scikit-feature/datasets.html.

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Authors and Affiliations

  1. CETC Key Laboratory of Avionics Information System Technology, Southwest China Institute of Electronic Technology, Chengdu, 610036, China

    Tao Xiang, Peng Li & Jie Liu

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Liang Tian & Mao Ye

Authors
  1. Tao Xiang
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  2. Liang Tian
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  3. Peng Li
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  4. Jie Liu
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  5. Mao Ye
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Corresponding author

Correspondence to Liang Tian .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Xiang, T., Tian, L., Li, P., Liu, J., Ye, M. (2024). Unsupervised Feature Selection Using Both Similar and Dissimilar Structures. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_2

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  • DOI: https://doi.org/10.1007/978-981-99-8126-7_2

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