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Discriminative unsupervised 2D dimensionality reduction with graph embedding

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Abstract

Dimensionality reduction is a great challenge in high dimensional unlabelled data processing. The existing dimensionality reduction methods are prone to employing similarity matrix and spectral clustering algorithm. However, the noises in original data always make the similarity matrix unreliable and degrade the clustering performance. Besides, existing spectral clustering methods just focus on the local structures and ignore the global discriminative information, which may lead to overfitting in some cases. To address these issues, a novel unsupervised 2-dimensional dimensionality reduction method is proposed in this paper, which incorporates the similarity matrix learning and global discriminant information into the procedure of dimensionality reduction. Particularly, the number of the connected components in the learned similarity matrix is equal to cluster number. We compare the proposed method with several 2-dimensional unsupervised dimensionality reduction methods and evaluate the clustering performance by K-means on several benchmark data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

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Acknowledgments

This work was jointly supported by Natural Science Basic Research Plan in Shannxi Province of China No. 2017JM6056, and Designing inter-core Datapath for voltage frequency island mpSoC No. 15JK1726.

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Jun Guo, Xiaowei Zhao, Xuan Yuan & Yao Peng

  2. Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, 1111, Hungary

    Yangyuan Li

Authors
  1. Jun Guo
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  2. Xiaowei Zhao
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  3. Xuan Yuan
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  4. Yangyuan Li
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  5. Yao Peng
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Correspondence to Jun Guo or Yao Peng.

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Guo, J., Zhao, X., Yuan, X. et al. Discriminative unsupervised 2D dimensionality reduction with graph embedding. Multimed Tools Appl 77, 3189–3207 (2018). https://doi.org/10.1007/s11042-017-5019-9

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  • DOI: https://doi.org/10.1007/s11042-017-5019-9

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