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A New Approach for Supervised Dimensionality Reduction

Author

Listed:
  • Yinglei Song

    (Jiangsu University of Science and Technology, Zhenjiang, China)

  • Yongzhong Li

    (Jiangsu University of Science and Technology, Zhenjiang, China)

  • Junfeng Qu

    (Clayton State University, Morrow, USA)

Abstract

This article develops a new approach for supervised dimensionality reduction. This approach considers both global and local structures of a labelled data set and maximizes a new objective that includes the effects from both of them. The objective can be approximately optimized by solving an eigenvalue problem. The approach is evaluated based on a few benchmark data sets and image databases. Its performance is also compared with a few other existing approaches for dimensionality reduction. Testing results show that, on average, this new approach can achieve more accurate results for dimensionality reduction than existing approaches.

Suggested Citation

  • Yinglei Song & Yongzhong Li & Junfeng Qu, 2018. "A New Approach for Supervised Dimensionality Reduction," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 14(4), pages 20-37, October.
    • Handle: RePEc:igg:jdwm00:v:14:y:2018:i:4:p:20-37
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