Computer Science ›› 2020, Vol. 47 ›› Issue (10): 108-113.doi: 10.11896/jsjkx.190700112

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

Sparse Non-negative Matrix Factorization Algorithm Based on Cosine Similarity

ZHOU Chang1,2, LI Xiang-li1,3, LI Qiao-lin1, ZHU Dan-dan1, CHEN Shi-lian1, JIANG Li-rong1   

  1. 1 School of Mathematics and Computing Science,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    2 Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    3 Guangxi Key Laboratory of Cryptography and Information Security,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2019-07-17 Revised:2019-10-18 Online:2020-10-15 Published:2020-10-16
  • About author:ZHOU Chang,born in 1998,postgra-duate.His main research interests include image clustering and so on.
    LI Xiang-li,born in 1977,Ph.D,professor.Her main research interests include image clustering,non-negative matrix factorization,and optimization
  • Supported by:
    National Natural Science Foundation of China (11961010,71561008),Guangxi Natural Science Foundation (2018GXNSFAA138169),Guangxi Key Laboratory of Cryptography and Information Security (GCIS201708),Guangxi Key Laboratory of Automatic Testing Technology and Instruments (YQ19111,YQ18112) and Guangxi University Students Innovation and Entrepreneurship Project(201810595218)

Abstract: When the basic non-negative matrix factorization is applied to image clustering,the processing of abnormal points is not robust enough and the sparsity is poor.In order to improve the sparsity of the factorized matrix,the L2,1 norm is introduced into the basic non-negative matrix factorization,and the basic non-negative matrix factorization model is improved to achieve sparsity and improve the performance of the algorithm.At the same time,in order to reduce the correlation between the features and enhance the independence of the features of the non-negative matrix factorization model,the cosine similarity is introduced,and a sparse non-negative matrix factorization algorithm based on cosine similarity is proposed.The algorithm has significant advantages in high-dimensional data processingand feature extraction,and can improve the discrimination accuracy of the algorithm in ima-ge clustering.The experimental results show that the proposed new algorithm outperforms the traditional non-negative matrix factorization algorithm in a series of evaluation indicators.

Key words: Cosine similarity, Image clustering, L2,1 norm, Non-negative matrix factorization

CLC Number: 

  • O235
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