Computer Science ›› 2020, Vol. 47 ›› Issue (7): 71-77.doi: 10.11896/jsjkx.200200106

• Database & Big Data & Data Science • Previous Articles     Next Articles

Nonnegative Matrix Factorization Algorithm with Hypergraph Based on Per-treatments

LI Xiang-li1,2,3, JIA Meng-xue1,4   

  1. 1 School of Mathematics & Computing Science,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    2 Guangxi Key Laboratory of Cryptography and Information Security,Guilin,Guangxi 541004,China
    3 Guangxi Key Laboratory of Automatic Testing Technology and Instrument,Guilin,Guangxi 541004,China
    4 Guangxi University Key Laboratory of Data Analysis and Calculation,Guilin,Guangxi 541004,China
  • Received:2020-01-24 Online:2020-07-15 Published:2020-07-16
  • About author:LI Xiang-li,born in 1977,Ph.D,professor.Her main research interests include image clustering,nonnegative matrix factorization,and optimization.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (11961010,61967004),Guangxi Natural Science Foundation (2018GXNSFAA138169),Guangxi Key Laboratory of Cryptography and Information Security (GCIS201708),Guangxi Key Laboratory of Automatic Testing Technology and Instruments (YQ19111) and Innovation Project of GUET Graduate Education (2020YCXS087).

Abstract: With the development of the media technology,more information is stored as the pictures.It is a topic problem in the machine learning field that how to distribute the right label to lots of unsigned pictures.And the image clustering has wide application on the face recognition and the handwriting number recognition field.Because the pictures are always stored as nonnegative matrices,the nonnegative matrix factorization algorithm (NMF) plays an important role in the image clustering.But the disadvantage in NMF algorithm is that the algorithm processes the data in the original data space which may produce a terrible result when the data have errors.To address this problem,the proposed algorithm is the nonnegative matrix factorization algorithm with a hypergraph based on per-treatments (PHGNMF).The PHGNMF algorithm introduces the per-treatments and the hypergraph into the NMF algorithm.In the per-treatments,the algorithm uses the grayscale normalization to eliminate the influence of the different illuminations firstly and then the algorithm can extract the low-frequency information of the pictures by the wavelet analysis.The wavelet procession could also reduce the dimensions of the data.The algorithm constructs a hypergraph for the data to save the neighboring information which has an important influence in the clustering procession.At last the results in five fundamental data sets confirm the effectiveness of the algorithm compared with fundamental algorithms.The results show the increase of accuracy is 2%~7% and the increase of normalized mutual information on some data sets is 2%~5%.

Key words: Grayscale normalization, Hypergraph, Image clustering, Nonnegative matrix factorization, Wavelet analysis

CLC Number: 

  • TP391.4
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