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A New Approach for Supervised Dimensionality Reduction

A New Approach for Supervised Dimensionality Reduction

Yinglei Song, Yongzhong Li, Junfeng Qu
Copyright: © 2018 |Volume: 14 |Issue: 4 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522542674|DOI: 10.4018/IJDWM.2018100102
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MLA

Song, Yinglei, et al. "A New Approach for Supervised Dimensionality Reduction." IJDWM vol.14, no.4 2018: pp.20-37. http://doi.org/10.4018/IJDWM.2018100102

APA

Song, Y., Li, Y., & Qu, J. (2018). A New Approach for Supervised Dimensionality Reduction. International Journal of Data Warehousing and Mining (IJDWM), 14(4), 20-37. http://doi.org/10.4018/IJDWM.2018100102

Chicago

Song, Yinglei, Yongzhong Li, and Junfeng Qu. "A New Approach for Supervised Dimensionality Reduction," International Journal of Data Warehousing and Mining (IJDWM) 14, no.4: 20-37. http://doi.org/10.4018/IJDWM.2018100102

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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.

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