Abstract In the existing non-negative matrix factorization(NMF) methods, low-dimensional repre-sentation is directly computed on the original high-dimensional image dataset. Besides, NMF methods are sensitive to noise data, noise labels, unreliable graphs and poor in robustness. To solve these problems, a non-negative low rank graph embedding algorithm based on L21 norm(NLGEL21) is proposed. NLGEL21 takes the effective low rank structure and geometric information of the original dataset into account. L21 norm is introduced into the function of graph embedding and data reconstruction to further improve its robustness. In addition, a multiplicative iteration formula and convergence proof for NLGEL21 are produced. Experiments on ORL, CMU PIE and YaleB face databases show the superiority of NLGEL21.
Fund:Supported by National Natural Science Foundation of China(No.61572033,71371012,61976005), Innovation Team Project of Anhui Polytechnic University(No.4)
Corresponding Authors:
LU Guifu, Ph.D., professor. His research interests include artificial intelligence and pattern recognition.
About author:: LIU Guoqing, master student. His research interests include machine intelligence and pattern recognition;ZHANG Qiang, master student. His research interests include data mining and machine learning;ZHOU Sheng, master student. His research interests include machine learning.
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2019, Vol. 32 
