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Critical data points-based unsupervised linear dimension reduction technology for science data

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Abstract

Recent advances in machine learning and data mining have led to powerful methods for the analysis and visualization of high dimensional data. This paper proposes an unsupervised linear dimension reduction algorithm named critical points preserving projection (CPPP). Selecting some key data points to represent the others has become more and more popular owing to its effectiveness and efficiency. Rather than considering all data points equally, the proposed algorithm just preserves both local neighborhood structure and global variance of critical data points. We explore a joint modification of locality preserving projection and principal component analysis to achieve these objectives. Experimental results on the UCI data sets show its good performance on pattern classification.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets.html.

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Acknowledgments

The authors would like to thank the associate editor and all reviewers for their helpful comments and suggestions.

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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, China

    Di Wu & Chuanhe Huang

  2. School of Computer Science, Colorado Technical University, Colorado Springs, CO, USA

    Naixue Xiong

  3. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Jinrong He

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  1. Di Wu
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  2. Naixue Xiong
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  3. Jinrong He
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  4. Chuanhe Huang
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Correspondence to Chuanhe Huang.

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Wu, D., Xiong, N., He, J. et al. Critical data points-based unsupervised linear dimension reduction technology for science data. J Supercomput 72, 2962–2976 (2016). https://doi.org/10.1007/s11227-015-1421-0

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  • DOI: https://doi.org/10.1007/s11227-015-1421-0

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