Semi-supervised Non-negative Matrix FactorizationBased on Semi-tensor Product

WANG Lin; LI Li-Xiang; PENG Hai-Peng; YANG Yi-Xian

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Semi-supervised Non-negative Matrix FactorizationBased on Semi-tensor Product

WANG Lin 1, LI Li-Xiang 1 *, PENG Hai-Peng 2, YANG Yi-Xian 2

1.School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876

2. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876

*Correspondence author

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Funding: National Key R\&D Program of China Foundation （No.2016YFB0800602）

Opened online: 6 January 2020

Accepted by: none

Citation: WANG Lin, LI Li-Xiang, PENG Hai-Peng.Semi-supervised Non-negative Matrix FactorizationBased on Semi-tensor Product[OL]. [ 6 January 2020] http://en.paper.edu.cn/en_releasepaper/content/4750401

Non-negative matrix factorization (NMF) is an effective feature extraction method. And the traditional NMF requires that the number of columns in the basis matrix is equal to the number of rows in the coefficient matrix, which imposes a great limitation on its engineering applications. Furthermore, some data in the practical applications may carry label information. These require novel methods to break this limitation and consider the influence of label information at the same time. Based on this idea, this paper proposes the semi-supervised non-negative matrix factorization based on semi-tensor product (TSNMF). The proposed method not only makes full use of the known label information, but also breaks through the limitation of dimension matching constraint in the traditional NMF, which can save storage space and improve the operation speed of the TSNMF method. Moreover, We evaluate the classification performance of the TSNMF method through numerical experiments in ORL face database and JAFFE face database. The experimental results show that the proposed TSNMF method is superior to the semi-supervised non-negative matrix factorization (SNMF).

Keywords:artificial intelligence; semi-tensor product; semi-supervised; non-negative matrix factorization; dimension matching constraint

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