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Deep semi-nonnegative matrix factorization with elastic preserving for data representation

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Abstract

Deep matrix factorization methods can automatically learn the hidden representation of high dimensional data. However, they neglect the intrinsic geometric structure information of data. In this paper, we propose a Deep Semi-Nonnegative Matrix Factorization with Elastic Preserving (Deep Semi-NMF-EP) method by adding two graph regularizers in each layer. Therefore, the proposed Deep Semi-NMF-EP method effectively preserves the elasticity of data and thus can learn a better representation of high-dimensional data. In addition, we present an effective algorithm to optimize the proposed model and then provide its complexity analysis. The experimental results on the benchmark datasets show the excellent performance of our proposed method compared with other state-of-the-art methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China [Grant No. 61603159, 61672265], Natural Science Foundation of Jiangsu Province [Grant No. BK20160293], China Postdoctoral Science Foundation [Grant No. 2017 M611695] and Jiangsu Province Postdoctoral Science Foundation [Grant No. 1701094B].

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Authors and Affiliations

  1. School of Computer Engineering, Jiangsu University of Technology, Changzhou, China

    Zhen-qiu Shu, Cong-zhe You & Hong-hui Fan

  2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, China

    Zhen-qiu Shu, Xiao-jun Wu & Cong Hu

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  1. Zhen-qiu Shu
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  2. Xiao-jun Wu
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  3. Cong Hu
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  4. Cong-zhe You
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  5. Hong-hui Fan
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Correspondence to Zhen-qiu Shu.

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Shu, Zq., Wu, Xj., Hu, C. et al. Deep semi-nonnegative matrix factorization with elastic preserving for data representation. Multimed Tools Appl 80, 1707–1724 (2021). https://doi.org/10.1007/s11042-020-09766-w

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