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Dimension Reduction Based on Sampling

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Data Science (ICPCSEE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1879))

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Abstract

Dimension reduction provides a powerful means of reducing the number of random variables under consideration. However, there were many similar tuples in large datasets, and before reducing the dimension of the dataset, we removed some similar tuples to retain the main information of the dataset while accelerating the dimension reduction. Accordingly, we propose a dimension reduction technique based on biased sampling, a new procedure that incorporates features of both dimensional reduction and biased sampling to obtain a computationally efficient means of reducing the number of random variables under consideration. In this paper, we choose Principal Components Analysis(PCA) as the main dimensional reduction algorithm to study, and we show how this approach works.

This paper was supported by The National Key Research and Development Program of China (2020YFB1006104), The Opening Project of Intelligent Policing Key Laboratory of Sichuan Province (ZNJW2023KFZD004), Sichuan Police College (CJKY202001) and NSFC grant (62232005).

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Author information

Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Zhuping Li, Donghua Yang, Mengmeng Li, Haifeng Guo & Hongzhi Wang

  2. ConDB, Beijing, China

    Tiansheng Ye

Authors
  1. Zhuping Li
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  2. Donghua Yang
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  3. Mengmeng Li
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  4. Haifeng Guo
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  5. Tiansheng Ye
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  6. Hongzhi Wang
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Corresponding author

Correspondence to Donghua Yang .

Editor information

Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Zhiwen Yu

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Northwestern Polytechnical University, Xi'an, China

    Bin Guo

  5. Shiga University, Shiga, Japan

    Xiaokang Zhou

  6. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  7. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, Z., Yang, D., Li, M., Guo, H., Ye, T., Wang, H. (2023). Dimension Reduction Based on Sampling. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_15

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  • DOI: https://doi.org/10.1007/978-981-99-5968-6_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5967-9

  • Online ISBN: 978-981-99-5968-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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