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Dual local learning regularized NMF with sparse and orthogonal constraints

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Abstract

Non-negative matrix factorization (NMF) has shown remarkable competitiveness in the past few years. To fully exploit various known prior knowledge hidden in data, this paper proposes a dual local learning regularized NMF with sparse and orthogonal constraints (DLLNMF-SO) algorithm. DLLNMF-SO constructs two local learning regularizers to consider the geometric structure and discriminative information embedded in data and feature space, respectively. Besides, it makes full use of sparse self-representation information by adding the l2,1-norm constraint. Meanwhile, the orthogonal constraint is imposed on the basis vectors to preserve the correspondence between samples and basic vectors. We give an efficient iterative updating scheme for the optimization problem of DLLNMF-SO and provides its convergence guarantee. We demonstrate that our proposed approach outperforms other competitors by conducting serval experiments on three benchmark datasets.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant No. 61603159, 61902160, U21B2027, 62162033], Yunnan Fundamental Research Projects [Grant No. 202101BE070001–056, 202101AT070438], Yunnan Provincial Major Science and Technology Special Plan Projects [Grant No. 202002AD080001, 202103AA080015].

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Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Zhenqiu Shu

  2. School of Computer Engineering, Jiangsu University of Technology, Changzhou, 231001, China

    Furong Zuo, Wenli Wu & Congzhe You

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  1. Zhenqiu Shu
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  2. Furong Zuo
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  3. Wenli Wu
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  4. Congzhe You
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Correspondence to Zhenqiu Shu.

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Shu, Z., Zuo, F., Wu, W. et al. Dual local learning regularized NMF with sparse and orthogonal constraints. Appl Intell 53, 7713–7727 (2023). https://doi.org/10.1007/s10489-022-03881-x

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