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Double graphs-based discriminant projections for dimensionality reduction

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Abstract

Graph embedding plays an important role in dimensionality reduction for processing the high-dimensional data. In graph embedding, its keys are the different kinds of graph constructions that determine the performance of dimensionality reduction. Inspired by this fact, in this article we propose a novel graph embedding method named the double graphs-based discriminant projections (DGDP) by integrating two designed discriminative global graph constructions. The proposed DGDP can well discover the discriminant and geometrical structures of the high-dimensional data through the informative graph constructions. In two global graph constructions, we consider the geometrical distribution of each point on each edge of the graphs to define the adjacent weights with class information. Moreover, in the weight definition of one graph construction, we further strengthen pattern discrimination among all the classes to design the weights of the corresponding adjacent graph. To demonstrate the effectiveness of the proposed DGDP, we experimentally compare it with the state-of-the-art graph embedding methods on several data sets. The experimental results show that the proposed graph embedding method outperforms the competing methods with more power of data representation and pattern discrimination in the embedded subspace.

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Notes

  1. https://www.cs.ucr.edu/eamonn/time_series_data/.

  2. https://archive.ics.uci.edu/ml/index.php.

  3. http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html.

  4. https://fei.edu.br/~cet/facedatabase.html.

  5. http://www.cl.cam.ac.uk/research/dtg/attarchive/face-database.html.

  6. http://www4.comp.polyu.edu.hk/biometrics/.

  7. https://www.kaggle.com/bistaumanga/usps-dataset.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61976107, 61672268, 61962010 and 61502208), International Postdoctoral Exchange Fellowship Program of China Postdoctoral Council (No. 20180051), Research Foundation for Talented Scholars of JiangSu University (Grant No. 14JDG037), Open Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grant No. 2017RYJ04), Application Foundation of Sichuan Province (Grant No. 2018JY0386) Natural Science Foundation of Guizhou Province (Nos.[2017]1130 and [2017]5726-32), Natural Science Foundation of Ningxia of China (No. 2019AAC03122), and Key Science and Research Project of North Minzu University (No. 2019KJ43).

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, 212013, Jiangsu, People’s Republic of China

    Jianping Gou, Ya Xue, Yongzhao Zhan & Jia Ke

  2. College of Electrical and Information Engineering, North Minzu University, Yinchuan, 750021, Ningxia, People’s Republic of China

    Hongxing Ma

  3. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, 643000, Sichuan, People’s Republic of China

    Yong Liu

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  1. Jianping Gou
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  2. Ya Xue
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  3. Hongxing Ma
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  4. Yong Liu
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  5. Yongzhao Zhan
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Correspondence to Jianping Gou.

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Gou, J., Xue, Y., Ma, H. et al. Double graphs-based discriminant projections for dimensionality reduction. Neural Comput & Applic 32, 17533–17550 (2020). https://doi.org/10.1007/s00521-020-04924-5

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