[1]XU Huimin,CHEN Xiuhong.Graph-regularized, sparse discriminant, non-negative matrix factorization[J].CAAI Transactions on Intelligent Systems,2019,14(6):1217-1224.[doi:10.11992/tis.201811021]
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Graph-regularized, sparse discriminant, non-negative matrix factorization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1217-1224 Column: 学术论文—人工智能基础 Public date: 2019-11-05
Title:
Graph-regularized, sparse discriminant, non-negative matrix factorization
Author(s):
XU Huimin;  CHEN Xiuhong
School of Digital Media,Jiangnan University,Wuxi 214000,China
Keywords:
non-negative matrix factorization; feature extraction; dimensionality reduction; manifold learning; maximum margin criterion; discriminant information; sparse constraints; linear representation
CLC:
TP391.4
DOI:
10.11992/tis.201811021
Abstract:
Non-negative matrix factorization is a popular data representation method. Using graph regularization constraints can effectively reveal the local manifold structure between data. In order to better extract image features, a graph-regularized, sparse-discriminant, non-negative matrix factorization algorithm is proposed in this paper. The sparse linear representation between similar samples was used to construct the corresponding graph and weight matrix. The objective function using the maximum margin criterion with L2,1 -norm constraint was optimized, using the tag information of the dataset to maintain the manifold structure of samples and discrimination of characteristics, and the iterative update rules of the algorithm are given. Experiments were carried out on the ORL, AR, and COIL20 datasets. Compared with other algorithms, GSDNMF-L2,1 showed higher classification accuracy in feature extraction.
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