A New Approach for Supervised Dimensionality Reduction

Yinglei Song, Yongzhong Li and Junfeng Qu
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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

International Journal of Data Warehousing and Mining (IJDWM), 2018, vol. 14, issue 4, 20-37

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:14:y:2018:i:4:p:20-37

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