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Discriminative and orthogonal subspace constraints-based nonnegative matrix factorization
Li, Xuelong1; Cui, Guosheng2; Dong, Yongsheng3
Department光学影像学习与分析中心
2018-11
Source PublicationACM Transactions on Intelligent Systems and Technology
ISSN21576904;21576912
Volume9Issue:6
Contribution Rank1
AbstractNonnegative matrix factorization (NMF) is one widely used feature extraction technology in the tasks of image clustering and image classification. For the former task, various unsupervised NMF methods based on the data distribution structure information have been proposed. While for the latter task, the label information of the dataset is one very important guiding. However, most previous proposed supervised NMF methods emphasis on imposing the discriminant constraints on the coefficient matrix. When dealing with new coming samples, the transpose or the pseudoinverse of the basis matrix is used to project these samples to the low dimension space. In this way, the label influence to the basis matrix is indirect. Although, there are also some methods trying to constrain the basis matrix in NMF framework, either they only restrict within-class samples or impose improper constraint on the basis matrix. To address these problems, in this article a novel NMF framework named discriminative and orthogonal subspace constraints-based nonnegative matrix factorization (DOSNMF) is proposed. In DOSNMF, the discriminative constraints are imposed on the projected subspace instead of the directly learned representation. In this manner, the discriminative information is directly connected with the projected subspace. At the same time, an orthogonal term is incorporated in DOSNMF to adjust the orthogonality of the learned basis matrix, which can ensure the orthogonality of the learned subspace and improve the sparseness of the basis matrix at the same time. This framework can be implemented in two ways. The first way is based on the manifold learning theory. In this way, two graphs, i.e., the intrinsic graph and the penalty graph, are constructed to capture the intra-class structure and the inter-class distinctness. With this design, both the manifold structure information and the discriminative information of the dataset are utilized. For convenience, we name this method as the name of the framework, i.e., DOSNMF. The second way is based on the Fisher's criterion, we name it Fisher's criterion-based DOSNMF (FDOSNMF). The objective functions of DOSNMF and FDOSNMF can be easily optimized using multiplicative update (MU) rules. The new methods are tested on five datasets and compared with several supervised and unsupervised variants of NMF. The experimental results reveal the effectiveness of the proposed methods. ? 2018 ACM.
DOI10.1145/3229051
Indexed ByEI
Language英语
PublisherAssociation for Computing Machinery
EI Accession Number20190306372647
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31104
Collection光学影像学习与分析中心
Affiliation1.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China;
2.University of Chinese Academy of Sciences, 19A Yuquanlu, Beijing; 100049, China;
3.School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan; 471023, China
Recommended Citation
GB/T 7714
Li, Xuelong,Cui, Guosheng,Dong, Yongsheng. Discriminative and orthogonal subspace constraints-based nonnegative matrix factorization[J]. ACM Transactions on Intelligent Systems and Technology,2018,9(6).
APA Li, Xuelong,Cui, Guosheng,&Dong, Yongsheng.(2018).Discriminative and orthogonal subspace constraints-based nonnegative matrix factorization.ACM Transactions on Intelligent Systems and Technology,9(6).
MLA Li, Xuelong,et al."Discriminative and orthogonal subspace constraints-based nonnegative matrix factorization".ACM Transactions on Intelligent Systems and Technology 9.6(2018).
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