Robust semi non-negative low-rank graph embedding algorithm via the L21 norm-Reference-Cited by-同舟云学术

Robust semi non-negative low-rank graph embedding algorithm via the L21 norm

Published:2021-11-03 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Liu Guoqing,Ge Hongwei,JinlongYang ,Wang Shuangxi

Funder

Natural Science Foundation of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-021-02837-x.pdf

Reference33 articles.

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3. Gurve D, Delisle-Rodriguez D, Romero-Laiseca M et al (2020) Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition. J Neural Eng 17(2):026029.1–026029.15

4. Li DG, Chen X, Li HL et al (2019) Manifold regularized sparse constraint non-negative matrix factorization with superpixel algorithm for hyperspectral unmixing. J Comp Appl 39(10):3100–3106

5. Hu LR, Jia SY, Wu N (2020) Face recognition based on linear projective non-negative matrix factorization with Kullback-Leibler divergence. J Phys Conf Series 1437(1):012111

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