Sunday 24th of September 2017
 

Convergent Projective Non-negative Matrix Factorization


Lirui Hu, Jianguo Wu and Lei Wang

In order to solve the problem of algorithm convergence in projective non-negative matrix factorization (P-NMF), a method, called convergent projective non-negative matrix factorization (CP-NMF), is proposed. In CP-NMF, an objective function of Frobenius norm is defined. The Taylor series expansion and the Newton iteration formula of solving root are used. An iterative algorithm for basis matrix is derived, and a proof of algorithm convergence is provided. Experimental results show that the convergence speed of the algorithm is higher, however it is affected by the initial value of the basis matrix; relative to non-negative matrix factorization (NMF), the orthogonality and the sparseness of the basis matrix are better, however the reconstructed results of data show that the basis matrix is still approximately orthogonal; in face recognition, there is higher recognition accuracy. The method for CP-NMF is effective.

Keywords: Non-negative Matrix Factorization, Projective, Convergence, Face Recognition.

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ABOUT THE AUTHORS

Lirui Hu
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University School of Computer Science and Technology, Nantong University School of Computer Science and Technology, Anhui University

Jianguo Wu
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University School of Computer Science and Technology, Anhui University

Lei Wang
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University School of Computer Science and Technology, Anhui University


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